# Measuring the transition to regenerative agriculture in the UK with a co-designed experiment: design, methods and expected outcomes

Katherine Berthon, Coline C Jaworski, Jonathan D Beacham, Peter Jackson, Jonathan Leake, Niamh M Mchugh, Lucy Capstick, Tim Daniell, Anna Krzywoszynska, Duncan Cameron, et al.

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## To cite this version:

Katherine Berthon, Coline C Jaworski, Jonathan D Beacham, Peter Jackson, Jonathan Leake, et al. Measuring the transition to regenerative agriculture in the UK with a co-designed experiment: design, methods and expected outcomes. Environmental Research: Food Systems, 2024, 1 (2), pp.025007. 10.1088/2976-601X/ad7bbe hal-04869080

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# Measuring the transition to regenerative agriculture in the UK with a co-designed experiment: design, methods and expected outcomes

To cite this article: Katherine Berthon *et al* 2024 *Environ. Res.: Food Syst. 1* 025007

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# ENVIRONMENTAL RESEARCH
## FOOD SYSTEMS

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# Measuring the transition to regenerative agriculture in the UK with a co-designed experiment: design, methods and expected outcomes

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Katherine Berthon<sup>1,11,*</sup> ID, Coline C Jaworski<sup>2,11,*</sup> ID, Jonathan D Beacham<sup>8</sup> ID, Peter Jackson<sup>5</sup> ID, Jonathan Leake<sup>6</sup> ID, Niamh M McHugh<sup>7</sup> ID, Lucy Capstick<sup>7</sup>, Tim Daniell<sup>6</sup> ID, Anna Krzywoszynska<sup>3,4</sup> ID, Duncan Cameron<sup>5</sup> ID, John Holland<sup>7</sup> ID, Sue Hartley<sup>6</sup> ID, Nicolas Desneux<sup>2</sup>, Kelly Jowett<sup>9</sup> ID, Yu Zhao<sup>6</sup>, Penelope J Watt<sup>6</sup> and Lynn V Dicks<sup>1,10,*</sup> ID

E-mail: kb809@cam.ac.uk, coline.jaworski@inrae.fr and lv22@cam.ac.uk

**Keywords:** regenerative agriculture, codesign, biodiversity, soil health, sustainable transitions

Supplementary material for this article is available online

## Abstract

Regenerative agriculture is promoted as a farming system that can improve agricultural sustainability, address soil degradation, and provide ecosystem service benefits. However, there remains limited evidence for the quantifiable benefits of a widespread transition to regenerative agriculture on soil, biodiversity, and crop quality, particularly at the landscape scale, and poor integration of findings across disciplines. Social and cultural aspects of the transition, such as the positioning of regenerative agriculture as a grassroots movement, farmers' perspectives on defining regenerative practices, and social or political barriers to implementation, are harder to quantify and often overlooked in evidence-based approaches. Here, we present the detailed methodology for our interdisciplinary, co-designed landscape-scale experiment measuring changes in soil health, biodiversity, yield, and grain quality, as well as social and political dimensions of the implementation of regenerative practices. Our unique approach, through the co-production process, the landscape-scale, and the focus on a systemic transition instead of individual practices, will bring strong evidence of the benefits of regenerative agriculture for sustained agricultural productivity, the mitigation of climate change and biodiversity depletion in agroecosystems. Our research aims to guide future studies transforming theoretical ecology into testable hypotheses in real-world systems and provide actionable evidence to inform agricultural policies in the UK and beyond.

## 1. Introduction

Agricultural soil degradation is a global, unprecedented crisis, caused by intensive agriculture practices, including deep soil cultivation and synthetic inputs for annual, resource-demanding but high-yielding crops. Intensive farming practices have led to reduced agricultural productivity through loss of organic matter and soil structure, and to biodiversity loss (Graves et al 2015, Evans et al 2020, Raven and Wagner 2021). In

<footnote n="1">¹ Department of Zoology, University of Cambridge, Cambridge, United Kingdom</footnote>

<footnote n="2">² Université Côte d'Azur, INRAE, UMR ISA, Nice, France</footnote>

<footnote n="3">³ Department of Geography, University of Sheffield, Sheffield, United Kingdom</footnote>

<footnote n="4">⁴ History, Culture and Communications, University of Oulu, Oulu, Finland</footnote>

<footnote n="5">⁵ Institute for Sustainable Food, University of Sheffield, Sheffield, United Kingdom</footnote>

<footnote n="6">⁶ School of Biosciences, University of Sheffield, Sheffield, United Kingdom</footnote>

<footnote n="7">⁷ Game and Wildlife Conservation Trust, Fordingbridge, United Kingdom</footnote>

<footnote n="8">⁸ University of Bristol Business School, Bristol, United Kingdom</footnote>

<footnote n="9">⁹ Rothamsted Research, Harpenden, United Kingdom</footnote>

<footnote n="10">¹⁰ School of Biological Sciences, University of East Anglia, Norwich, United Kingdom</footnote>

<footnote n="11">¹¹ These authors equally contributed to this manuscript.</footnote>

<footnote>* Authors to whom any correspondence should be addressed.</footnote>
response to these issues and to address climate change, a range of alternative farming systems have been devised and evaluated, with organic farming, conservation agriculture, and regenerative agriculture concepts all emerging in the 1980–90 s (Holland et al 1994). Organic farming prohibits the use of synthetic inputs (pesticides and fertilisers) but has been criticised in its industrial form as failing to promote environmental processes and requiring more land to maintain agricultural production than conventional agriculture (Tscharntke et al 2021). Conservation agriculture has been promoted for soil sustainability, seeking to minimise soil disturbance through reducing tillage, providing permanent soil cover and diversified rotations (Hobbs et al 2008, Giller et al 2015). However, it has been criticised for its narrow focus on cropping operations and its reliance on costly technological solutions, preventing widespread adoption (Bless et al 2023).

Regenerative agriculture originated from the Rodale Institute in the USA in the 1980s (Rodale 1983). More recently, it has been promoted in response to frustrations with previous paradigms, and a desire to restore natural processes that support soil health and reduce dependence on costly inputs rather than a narrow focus on maintaining productivity (LaCanne and Lundgren 2018, Sherwood and Uphoff 2000, Tittonell et al 2022, Bless et al 2023). In the UK context, regenerative agriculture seeks to achieve its goals by mobilising nature-based solutions instead of artificial inputs, commonly through adherence to five key principles: (i) reduce soil disturbance, (ii) increase crop diversity, (iii) keep the soil covered, (iv) keep living roots all year round, and (v) introduce livestock (Ritz 2021, Farm of the Future: Journey to Net Zero 2022, Khangura et al 2023). These practices enable the regeneration of root-derived carbon in much larger amounts than provided by annual crops, and enhancement of soil biodiversity, in turn supporting mycorrhizal fungi and symbiotic nitrogen fixation to reduce requirements for phosphorus and nitrogen fertilizers. Increases in earthworm and mycorrhizal activity regenerate soil macroaggregates (soil structure), and therefore soil carbon sequestration capacity (Guest et al 2022). These soil chemical and biological changes are expected to have broader environmental benefits, including climate change mitigation, improvements to water quality and biodiversity (Fenster et al 2021, O'Donoghue et al 2022); as well as economic and human health impacts through changing yields, input use, and nutritional quality of crops (Montgomery et al 2022, Khangura et al 2023).

The regenerative agriculture concept is gaining political importance, as shown by the peer-reviewed grant awards to major research programmes funded by UK Research and Innovation (Jackson et al 2021, Doherty et al 2022), but also in the EU with a recent assessment of regenerative agriculture by the European Academies Science Advisory Council (EASAC policy report 44, 2022). It is also considered an important way to reach ‘Net Zero Emissions’ goals, through enhanced carbon sequestration in agricultural soils, while also improving agricultural sustainability (Defra 2023, Khangura et al 2023). Aspects of regenerative agriculture are being incentivised under UK agricultural policy and in particular the Sustainable Farming Incentive (SFI) of the current UK Environmental Land Management schemes (ELMs) for England (Defra 2023). Notably, SFI schemes subsidise practices that are considered to increase carbon sequestration such as introducing herbal leys in arable rotations and the use of winter cover crops (Lal et al 2007, Virto et al 2015).

However, the grassroots origin of regenerative agriculture has led to various and often incongruous definitions of regenerative practices and principles (Newton et al 2020, Bless et al 2023, Jaworski et al 2023a). On the one hand, regenerative agriculture can be defined as an open-ended and potentially radical farmer-led paradigm shift (Beacham et al 2023), and on the other, a process and outcomes-based approach to farming (Newton et al 2020) that can be quantified and normalised in progressive policy agendas (Gordon et al 2023). Consideration of regenerative agriculture as a movement values flexibility in definition of regenerative practices, allowing for adaptation to the local context (O'Donoghue et al 2022), and adoption of different suites of practices by farmers in different environments (Jaworski et al 2023a) that do not necessarily share core values (Tittonell et al 2022). Flexibility in definition, combined with limited scientific evidence for the link between process and outcomes (e.g. reduced tillage leading to increased soil carbon sequestration, Tittonell et al 2022) has prevented a unified definition of which practices contribute to restoring soil health and should be promoted as part of the movement (Tittonell et al 2022, Khangura et al 2023).

Therefore, understanding the efficacy of a transition to regenerative agriculture requires measuring whole-system changes, where farmers fully redesign their farming system to incorporate new crops and farming practices, as well as reshaping perceptions, value-systems, and worldviews (Gordon et al 2022, Miller-Klugesherz and Sanderson 2023, Seymour and Connelly 2023). This entails the adoption of a combination of practices that are expected to act synergistically (Li et al 2020, Jaworski et al 2023a), as well as understanding the economic and pragmatic incentives to their adoption (Beacham et al 2023). However, most practices have only been tested individually, at best at the field scale (reviewed in Khangura et al 2023), and less quantifiable aspects of the transition such as motivations, interrelationships and regenerative mindsets are rarely considered in process-based definitions (Gordon et al 2023, Jaworski et al 2023a). In addition, many of the agroecological processes that regenerative agriculture could restore, including
<figure><img src="image_7.png" /><figcaption>**Figure 1.** Outline of the study objectives and transdisciplinary approach.</figcaption></figure>

provisioning and regulating ecosystem services such as biological pest control and pollination play out at larger spatial scales (Rusch *et al* 2016, Harrison *et al* 2019, Jaworski *et al* 2022), and require translating field-scale outcomes to landscape-scale processes.

We argue that measuring the systemic transition to regenerative agriculture must be achieved through locally-based knowledge co-production between farmers and researchers. As a farmer-led movement, regenerative agriculture centres farmers both as knowledge producers, leading the inquiry into the efficacy and development of regenerative farming practice (Krzywoszynska 2019) as well as decision-makers, adapting their farm systems to this new direction (Kleijn et al 2019, Norström et al 2020). Quantifying the impacts of regenerative agriculture practices is seen as important by regenerative agriculture practitioners, as it will enable them to benefit from shifting policy and market frameworks (Krzywoszynska 2019).

Here, we present our transdisciplinary, co-design approach to quantify the broad environmental benefits of a transition to regenerative agriculture from conventional arable and mixed farming practices. While we engage with a process-based definition of regenerative farming, we worked together with two groups of UK farmers to design a tailored set of practices that did not lose sight of regenerative agriculture as a movement. We approach this study in a holistic manner integrating ecological benefits (impacts on soil health, biodiversity, and ecosystem services), economic factors (yield, input costs and crop quality), and the social, political, and cultural dimensions of the transition (figure 1). We work beyond field scale with a replication unit of 60 ha, which captures shifts in agricultural rotations and farming functioning across fields, and, where the theoretically optimal design is adapted to fit real-world farming (Lacoste et al 2022). Specifically, we investigate the following questions:

1. What is the impact of a systemic transition to regenerative agriculture on key soil physical, chemical, and biological properties?

2. What are the wider environmental benefits of such a transition for biodiversity and associated ecosystem services, using farmland birds, pollinators, insect pests and natural enemies as indicators?

3. What is the effect of such a transition on cereal crop yields and grain quality?

4. What are farmers' perspectives on regenerative agriculture in the current policy and market environments?

## 2. Material and methods

We established a four-year co-designed quasi-experiment in 25 sites managed by 17 farmers across two landscapes in the UK. The co-production process is enabled by fortnightly meetings of a working group, composed of farmer cluster leaders and coordinators, and scientists from social sciences, soil sciences, and ecology. This experiment has been approved by an ethical agreement (ref. PRE.2021.055 delivered by the Cambridge Psychology Research Ethics Committee on 10 August 2021). The experiment itself started after harvest in August 2021, and will continue for four years (until harvest 2025). Years 1–4 are defined from
post-harvest to the next harvest. Data has been collected in Year 1, the baseline year (August–September 2021 to July–August 2022); new datasets will be collected in Years 3 and 4 for all metrics except otherwise stated below and following the same methodology.

**2.1. Farmer cluster selection**

To achieve strong and consistent input into the research from farmer participants and design a
landscape-scale experiment, the research relied on farmer clusters, i.e. voluntary farmer associations
dedicated to knowledge exchange between farmers in a specific local area (GWCT 2019). We used the
following criteria to guide the selection of farmer clusters; ideally farmer clusters should:

* encompass a diversity of farming systems, from more conventional to more regenerative;

* have a historic interest and enthusiasm for sustainable agriculture in general, and for regenerative agriculture in particular;

* have established working relationships with researchers with sites accessible for frequent visits.

Two farmer clusters were formally enrolled in the research: one in the East and one in the Southwest of England. These farmer clusters represent a diversity of farming systems, including arable only farms and mixed arable and livestock farms. These farms may also experience different climates (the Southwest has higher rainfall and colder winters) and are situated on different soil types (either thin, chalk-dominated soils, or heavy clay soils). An academic team met with each farmer cluster in July 2021 to share the objectives and requirements of the experiment and collect informed consent, assuring full anonymity of all data collected, but also participants' right to retract at any time. For confidentiality reasons, neither the farmer clusters nor the individual farms or farmers are identified here.

**2.2. Experimental design and site selection**

The first co-production step was to establish a list of regenerative agriculture practices, the effects of which would be studied. Rather than prescribing an established set of principles and practices, we asked farmers to assemble a list of practices that they considered to represent regenerative farming within their farming contexts. This list was refined by the working group, based on consensus across clusters, and potential for improvements to soil health (Lal *et al* 2007, Virto *et al* 2015). This resulted in a list of nine practices:

1. Use no-till/minimum non-inversion tillage, with direct drilling as a standard and not just on certain crops;

2. Use reduced compaction techniques e.g. low tyre PSI, control traffic farming;

3. Introduce crop diversity in the rotation (especially legumes);

4. Use multi-species cover and catch cropping in the rotation;

5. Increase spring cropping in the rotation;

6. Retain crop residues in field;

7. Introduce livestock to arable rotations;

8. Use organic manures/ green compost/ digestate/ compost;

9. Introduce herbal leys in the rotation.

To test the effects of changing practices on soil health and on biodiversity, and to understand how farmers make that systemic transition, a first experimental design was proposed by researchers. It followed a BACI design (before-after-control-impact; Osenberg et al 2006, Christie et al 2019) and consisted of three treatments each with four replicates per cluster:

* Control: conventional farming: characterised by short rotations with low crop diversity and intensive tillage;

* Regenerative: farmed according to the regenerative principles, using practices defined above for at least four years before the start of the study;

* Change (or 'impact' group): conventional farming shifting to regenerative farming after one year into the study (the 'before' baseline year) by adopting some combination of the nine practices above.

The regenerative group was added to the BACI design due to the short-term nature of the study, making it difficult to measure significant changes in soil structure and chemistry, and potentially biodiversity, in less than four years (Puerta et al 2018, Guest et al 2022). During the co-design process, the before-after structure was altered to accommodate a diversity of transition trajectories: farmers in the Change group could have already implemented some of the practices at the baseline year, and each Change farmer defines their own regenerative agenda (e.g. what practices they would adopt immediately after the baseline year, and what
<figure><img src="image_8.png" /><figcaption>**Figure 2.** Experimental design and layout of the experimental sites in the southwest cluster (A) and the eastern cluster (B). Each striped square represents a farm, and each coloured square represents one site of ≥60 ha of farmland. In the Southwest, farmers have contributed one to three sites each, in a paired design, whereas in the Eastern cluster, each farmer will be managing one site in the experiment. ‘C’ (white) Control group (farmed with conventional practices); ‘R’ (green) Regenerative group (farmed with regenerative practices); ‘T’ (yellow) treatment (Change) group, with practices changing from conventional to regenerative over the course of the experiment.</figcaption></figure>

practices they would implement later). These alterations make the timeline less stringent than in a theoretical BACI experiment, but still allow a farm effect to be modelled, in which measures repeated through time can be compared with earlier measures of the same farm only instead of being compared with a past mean effect across all farms.

In each cluster, farmers enrolled a part of the land they farm (hereafter ‘site’) in one of the three treatments (Control, Regenerative and Change). The site was to be ≥60 ha in size, and, ideally, composed of multiple fields instead of a single large field, each representing different stages in the rotation and a diversity of crops (including one winter wheat field for the first year, if possible). This led to a total of thirteen sites managed by six farmers, each providing access to between one and three sites, in the South-West cluster, and twelve sites managed by eleven farmers in the East cluster, each providing access to one site, except for one farmer who provided two sites on separate farms (figure 2).

## 2.3. Farming practices data

To get a better understanding of the legacy effects from past practices and monitor practices used in the experimental sites over the four years of the study, we collect information on cropping rotations, cultivation practices, and pesticide and fertiliser inputs per farm (where available) from 2016 to 2022 (‘historical’ practices) and 2023–2025 (during the study) on a per-field, per-year basis in each field of the 25 sites included in the landscape-scale experiment. This information is collected through a combination of farmer interviews, pdf-form surveys and from record-keeping software used by farmers (e.g. Gatekeeper), and includes crops grown, inputs used and implementation of the regenerative practices in section 2.2. We also map Countryside Stewardship options and other ELMs, and their rotations across the study period, as well as record some general information about the remainder of the farm outside the study area (site) but in less detail—i.e. presence of regenerative practices, farm size and type (arable or mixed). These data will be used to explain the variation in our observed biodiversity and soil health outcomes and capture the gradient of implementation of regenerative practices (section 3).

## 2.4. Farmer, societal and policy contexts to adoption of regenerative agriculture

To understand and contextualise change over time in mindsets as some farmers have adopted more regenerative practices, we conduct two rounds of one-on-one semi-structured interviews with farmers involved in the management of one or more farms in the study; once at the beginning of the project (Spring 2022) and once after the transition has occurred (Spring 2025). The first year of interviews established a baseline against which to compare changes over the next three years of the experiment. Interview topics included the nature of the farm business, their understanding of the term ‘regenerative agriculture’, sources of information and advice, the changing policy environment, and the future of UK farming (Beacham et al 2023). We also attended a series of farmer cluster meetings to understand how farmers talked amongst
themselves about the adoption of, and challenges associated with, regenerative practices. This included meetings with invited presentations from agronomists and other ‘experts’, arranged and organised by the farmers themselves.

In addition, we will undertake a series of 12–15 in-depth interviews with actors in the policy domain (representatives from state agencies, NGOs and charities) to understand their perspective on the changing policy environment in which farmers are operating. These will include the shift from the Common Agricultural Policy, based on payments per hectare of land under cultivation, to ELMs, based on the payment of ‘public money for public goods’ (Defra 2023). The interviews will allow us to identify the different ‘epistemic communities’ (Gough and Shackley 2001) at play in this terrain: these are communities that differ not only in terms of how problems around the status quo are constructed, but also in their objectives, core beliefs, and the extent to which regenerative agriculture represents a favourable response.

## 2.5. Quantifying the impacts of regenerative agriculture on soil health

In line with a recent recommendation to the UK Government Soil Health Enquiry, we have measured topsoil water-stable macroaggregates (>2 mm) and the proportion of soil organic C and N in these aggregates as an integrated measure of soil health in arable landscapes (Guest et al 2022). Water-stable macroaggregates, which are generated by the interacting effects of roots, earthworms, and mycorrhizal fungi, are the critical structures within which organic carbon and nitrogen are sequestered, and macropore space maintained, ensuring good soil infiltration, water storage and drainage (Guest et al 2022). They are therefore highly responsive, early indicators of improvements in soil health, including carbon sequestration, fertility-building, improved biological and hydrological functioning (Puerta et al 2018, Guest et al 2022). To corroborate biological enhancement, we measure earthworm biodiversity, and use bulk density profiles to assess soil compaction, along with pH, all of which are common indicators of the influence of farming practices on soil health (Hallam et al 2020, Liptzin et al 2022, Khangura et al 2023). Comparison of these measures will be used to establish guideline values for different soil types and a methodology for landscape-scale soil monitoring of topsoil health.

Soil is sampled in February–March of Years 1 and 4, when the soil is moist, and under optimal weather conditions (not rainy, temperatures between 8 °C and 15 °C). Soil sampling follows a standard ‘W’ shape composed of eight locations per field (figure 3(A)). For one field per site and at each of the eight locations, standard 100 cm³ bulk density cores are collected to four different depths: 0–7, 7–14, 14–21, and 21–28 cm, which cover the topsoil, and plough layer. Earthworm densities are sampled at the same time as the soil coring, at three locations per field (figure 3(A)) using the method by Römbke et al (2006). A block of topsoil of 18 × 18 × 20 cm is extracted and placed on a blue plastic sheet, and earthworms are sorted manually, starting with those visible on the walls of the pit. Earthworms are counted and identified as juveniles versus adults (presence of a clitellum). In addition, a Visual Evaluation of Soil Structure, hereafter VESS, score is performed on a 4 × 18 × 20 cm slice extracted from the same pit and following the method and scoring by Vidacycle (https://soils.vidacycle.com/soil-tests/vess-visual-evaluation-of-soil-structure/).

## 2.6. Quantifying the wider environmental benefits of regenerative agriculture

We investigate the potential benefits of regenerative agriculture on three aspects of biodiversity and their related ecosystem services: winter farmland birds, particularly farmland-specialist and insectivorous birds; insect natural enemies (spiders, carabid beetles, ladybirds, parasitoids, and hoverflies) and their related pest control functions; and pollinators and associated pollination services. We have chosen these groups as they are responsive to changes in land management (e.g. Zielonka et al 2024), have purported responses to the regenerative practices given in 2.2 (see section 3.2, table 3) and provide important ecosystem services to agriculture (Issacs et al 2009). For example, regenerative agriculture has the potential to reverse declining population trends in farmland birds (Robinson and Sutherland 2002) by recreating favourable habitats through use of diverse rotations, winter stubbles and cover crops, and less productive stages in the rotations with reduced inputs (e.g. herbal leys; that support greater invertebrate populations; Donald et al 2002). Below we provide in brief the sampling methods chosen as appropriate for each group, but for more information on detailed sampling protocols see the Supplementary Methods.

### 2.6.1. Farmland bird sampling

We focus on winter farmland birds because winter is typically the time when birds become more reliant on seed and plant food, and earthworms in fields (Holland et al 2006). Birds are monitored from November to February of Years 1, 3 and 4, using three timed 600 m transects placed in fixed locations across the sampling site (see supplementary material for more information on placement rules). Each transect is composed of one 200 m section of field-margin and two 200 m in-field sections, each at least 50 m away from field corners (figure 3(A)). This was established as the best compromise to (i) record the widest bird diversity but
<figure><img src="image_9.png" /><figcaption>**Figure 3.** (A) An example of the location of soil and biodiversity samplings in a field. Locations of soil sampling and bird transects are mapped precisely using ViewRanger, while the other sample locations are approximative. (B) A pitfall trap and (C) a pan trap used for natural enemy sampling.</figcaption></figure>

especially that of species using in-field habitat; (ii) exhaustively sample sites in a relatively limited time window (Wilson and Gillings 2002, Atkinson et al 2006); and (iii) maximise the detectability of the treatment effects (farming practices affect birds using in-field habitat; Donald et al 2002). The three transects per site can be used as statistical units, that is pseudo-replicates of bird foraging activity measures.

Sampling occurs twice per year, first in November–December and then in January–February, with all sites of each farmer cluster sampled over a maximum time period of one month to minimise temporal variability, and with surveys starting one hour after dawn and finishing one hour before dusk to avoid counting birds moving to/from roost sites. Weather conditions, start and end of time walk for each transect as well as sunrise and sunset times are also recorded. In addition, vegetation height and percentage of bare soil are recorded in a 0.5 × 0.5 m quadrat at the end of each transect section (12 quadrats per site) to account for reduced visibility of birds on the ground.

### 2.6.2. Pest and natural enemy sampling

As proxies to estimate the effects of farming practices on biological pest control services, we measure abundances of aphid pests and their natural enemies in cereal crops (Chaplin-Kramer et al 2013, Jaworski et al 2015, Holland et al 2021). We focus on cereals as the economically most important cash crop in both farmer clusters; cereal crops are important both in arable and mixed UK farming systems, comprising 76% of the land area cropped in the UK (Defra 2022); and break crops (i.e. crops used to break cereal diseases, and which come after multiple years of cereal cropping) often differ between regenerative (e.g. peas, beans, leys) and conventional rotations (e.g. oil-seed rape). We also target several natural enemy functional groups with contrasting ecologies to disentangle the various mechanisms of regenerative practices effects e.g. changing aphid resources, alternative resources, and reduced insecticides. This includes ladybirds (generalist predators but high preference for aphids; Sloggett 2008, Pan et al 2020), ground-dwelling predators (carabid beetles and spiders—often generalists but less efficient at controlling aphids; Lövei and Sunderland 1996, Pywell et al 2015), hoverflies (specialist aphid predators; Chabert and Sarthou 2017) and parasitoid wasps (specialist aphid parasitoids; Monticelli et al 2019).

When present in a site and in each sampling year, one field of winter wheat, winter barley and/or spring barley are selected for pest and natural enemy sampling. Unlike the bird transects, these are not fixed from year-to-year but follow cereal fields as they move in the rotation. We use a variety of sampling methods to
best capture abundances and species richness of pests and natural enemies (McCrawy 2018, figure 3(A)), i.e. pitfall trapping to measure the activity-density of and catch ground-dwelling predators (Hohbein and Conway 2018, figure 3(B)), pan trapping to measure the abundances of aphid parasitoid wasps and hoverflies (O'Connor et al 2019, figure 3(C)) and sweep-netting to assess aphid and parasitized aphid abundances, as well as ladybird abundances.

We use four sampling rounds per year to capture seasonal fluctuations of natural enemies throughout the cropping year (from crop emergence to harvest) and allow for estimating the major demographic trends: (i) late October to early November, when winter cereals have grown enough biomass to host quantifiable insect populations (i.e. 5–10 cm high); (ii) first three weeks of April; (iii) second half of May; and (iv) late June-early July (before harvest). For logistical reasons (unusually late harvest and sowing in 2022), the Autumn round was not carried out in Year 1. The Autumn round coincides with aphid immigration from grass margins and other green bridges into crop fields (first leaves emerged, growth stage GS13-GS19, plant height ~5–8 cm; Bayer Aphid Expert Guide 2013, AHDB Wheat Growth Guide 2021); at this time of the year the predominant natural enemies are spiders but there also remain parasitoid wasps, some beetle species and hoverflies looking for food resources and overwintering sites (Legrand et al 2004, Raymond et al 2014). In early April (growth stage GS31, winter wheat stem elongation), the crop starts to grow again after winter dormancy phase (AHDB Wheat Growth Guide 2021). This is also when natural enemies emerge from overwintering (Legrand et al 2004, Raymond et al 2014). In late May, pest and natural enemy populations are at their highest growth rate, while in late June, populations have reached their highest levels or started to decline again, due to the decline in suitable plant resources soon before harvest (Holland et al 2005, Ciss et al 2014, Raymond et al 2014).

### 2.6.3. Pollinator sampling

The main pollinator-dependent crops in our farming systems are oilseed-rape, beans, and other break crops (Defra 2022), which differ between control and regenerative sites and therefore cannot be used to compare the abundance and diversity of visiting pollinators nor pollination levels across the differently farmed landscapes. For this reason, we focused on a common non-crop pollinator-dependent plant, hawthorn *Crataegus monogyna* Jacq. (García and Chacoff 2007, Jacobs et al 2009) which allows pollinator monitoring in any year, irrespective of the crop rotation. This species is abundant in site hedgerows of both landscapes and has shown pollination deficiency, providing an opportunity to measure improvement due to higher levels of floral resources for pollinators (Jacobs et al 2009).

To quantify the abundance and diversity of pollinators in each site, transect walks and pan traps are used along one fixed hedgerow transect per site twice a year during Years 1, 3 and 4: at the time of hawthorn flowering peak (May), and of immature fruit development (July) (figure 3(A)). In most cases, the hedgerow with the highest abundance of hawthorn among the three hedgerows used in bird transects was used; if none of these three had hawthorn, an alternative hedgerow with the highest hawthorn abundance was selected. Each hedgerow transect is 60 m long, and hedgerow characteristics (adjacent habitat type, orientation, management) have been recorded. On each sampling period the transect is walked twice; first to record general abundance of pollinators, and then to record pollinators specifically visiting hawthorn flowers. Two pan traps are placed at each end of the hawthorn transect immediately after hawthorn pollination sampling (figures 3(A) and (C)), and placed in the field for 48 h. Pan trap devices are the same as those used for natural enemy sampling (section 2.6.2) but are used in a different temporal window.

To quantify how other flowering resources may affect hawthorn pollination levels, the abundances of all flowering species in the hedgerow as well as in the adjacent field margin are quantified in six replicates starting at position 0 of the transect and every 10 m. Hedgerow replicates are vertical quadrats of 1 × 2 m examined while facing the hedge, and the field margin replicates are 1 × 1 m horizontal quadrats laid on one side of the hedge on the hedge bank or field margin. In both quadrat types, all flowering species are identified, their number of flower units counted, and their percentage cover visually estimated (POMS 2023). In case of very abundant flower resources, the number of flower units is counted in a subsection of the plot only and then extrapolated. Counts in the vertical plot also include hawthorn. In vertical plots, the approximate height and width of the hedgerow are recorded. In the horizontal plots, the percentage cover of bare ground is recorded. In addition, the floral resources, and percent bare ground are recorded in a 2 m radius around each pan trap.

### 2.6.4. Fruit set and pollination efficacy

As a proxy for pollination service, we measured the fruit set of hawthorn plants within the hedgerows sampled for pollinators. We selected twenty groups of buds during the first pollinator sampling round (section 2.8), randomly positioned at intervals of 3 m and at height 0.5–2 m above the ground along the hawthorn transect (Jacobs et al 2009; figure 3(A)). Where this is not possible due to low hawthorn
abundance, groups of buds are randomly selected within hawthorn subsections. The number of buds and/or flowers in each group of buds are counted. In July as fruits start to develop, the number of immature fruits in each marked group of buds is counted (Jacobs et al 2009). This provides estimates of initial pollination rates. Indeed, in fruit-producing plants, abscission of unfertilized immature fruits—which may be due to inadequate pollination—occurs soon after flowering (Jacobs et al 2009). Immature fruits that are most likely to mature are those that result from outcrosses (successful pollination; Stephenson 1981).

### 2.6.5. Weed sampling

Reducing tillage may have implications for weed management on farms. Weeds are sampled in the same field as for pests and natural enemy sampling (section 2.6.2), once a year in the first two weeks of May. We use the diagonal sampling method to fairly represent weed density and diversity (Colbach et al 2000, Golafshan and Yasari 2012). That is, 30 m away from field corner, weeds are sampled in a 0.5*0.5 m quadrat every 20 m and repeated 20 times (figure 3(A)). If the field size was too small to place all quadrats in a diagonal, the last quadrats are placed along the second diagonal at least 30 m away from the field corner. The percent of coverage (in cm², accurate to 2 cm²) in the quadrat as well as the identification of each weed is recorded *in situ* using local botany guides: Hubbard (1992) for grasses, and Rose and O'Reilly (2006) for other flowering plants.

## 2.7. Yield and grain quality outcomes

To capture potential trade-offs between ecological and economic impacts of regenerative practices and to measure potential benefits of regenerative agriculture for human nutrition, in terms of agriculture product quality, we collect farmers' data on crop yields and quality from our experimental sites. At each harvest we also collect 1 kg of grain of winter wheat from each site (if available) and analyse for macro and micro-nutrients, mycotoxins and pesticide residues as indicators of grain quality. Using information on variable costs and yields, we also perform a basic economic comparison with the FarmBench online benchmarking tool.

## 2.8. Statistical considerations

### 2.8.1. Power analyses

Power analyses were conducted using R Core Team (2022) to define the sampling sizes for natural enemy and pollinator data based on expected size effects and standard deviations (number of pan traps, pitfall traps, and sweep netting bounds). We followed the approach developed by Breeze et al (2021). Independent power analyses were performed for the abundances of hoverflies, bees, aphids, ladybirds and carabid beetles, and tested the effects of the number of rounds, the number of replicates per field, and the range of expected size effects on the likelihood to detect a significant treatment effect (table 1). Due to the real-farm constraints which shaped the experimental design, a conservative approach was adopted, where each dataset was simulated across one landscape and eight sites with two treatments (control—4 sites: conventional farming; treatment—4 sites: expected effect sizes without considering the time since change in practices). Note that expected effect sizes listed in table 1 often relate to tillage intensity only, while regenerative systems are expected to differ along multiple dimensions relative to conventional systems, and therefore extrapolated effect sizes may be larger (section 3).

Final sampling sizes were established as a compromise between logistics (the highest sampling size feasible within time constraints), the likelihood to detect the expected effects, and environmental ethics: where adding more samples did not change the likelihood to detect an effect, the lowest sampling size was selected, to minimise the negative impact of sampling on biodiversity. For more detail on the power analysis implementation, see the Supplementary Material.

### 2.8.2. Arthropod identification methods

The identification methods for arthropods collected by pan- and pitfall-trapping entail trade-offs between taxonomic resolution and speed of processing samples. The objective is to design high-throughput, accurate methods to be able to process a very large number of specimens and obtain statistically and ecologically meaningful data, that is quantitative data with a large sampling size and in a limited budget and time. To this end, we focus on key taxonomic groups that are known to be associated with ecosystem service provision: Hymenoptera (containing pollinating bees, and parasitoids); Syrphidae (pollinating flies); Araneae (terrestrial spiders); and Coleoptera (predatory beetles). In table 2, we outline the expected resolution and accuracy achievable for identification in these groups, as well as the resources available.
<table><caption>**Table 1.** Summary of power analyses.</caption><thead><tr><th>Ecological metric</th><th>Expected size effect (%)</th><th>Power (%)</th><th>References</th></tr></thead><tbody><tr><td>Number of hoverflies per pan trap</td><td>+50 to +100</td><td>70–98</td><td>Chabert and Sarthou (2017), (Chabert and Sarthou 2020)</td></tr><tr><td>Number of bees per pan trap</td><td>+20 to +50</td><td>12–39</td><td>Williams et al (2010)</td></tr><tr><td>Number of aphids per 10 sweeps</td><td>– 45 to –75</td><td>73–95</td><td>(Kennedy et al (2010), Chabert and Sarthou (2017), (Chabert and Sarthou 2020)</td></tr><tr><td>Number of ladybugs per 10 sweeps</td><td>–25 to –35</td><td>21–50</td><td>Tillman et al (2004); Tamburini et al (2016)</td></tr><tr><td>Number of carabid beetles per pitfall trap</td><td>+36 to +300</td><td>21–100</td><td>Kosewska et al (2014); Jowett et al (2021)</td></tr></tbody></table>

<table><caption>**Table 2.** Identification of arthropod groups used as indicators in our study, the expected taxonomic resolution and accuracy achievable, and taxonomic keys used.</caption><thead><tr><th>Ecosystem Service Role</th><th>Biodiversity Indicator</th><th>Taxonomic Resolution</th><th>Accuracy of Identification (%)</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Natural Enemies</td><td>Spiders (Araneae)</td><td>Family</td><td>100</td><td>Jones-Walters (1989)</td></tr><tr><td>Beetles (Carabidae)</td><td>Species</td><td>90–95</td><td>Luff (2007), Jowett (2022)</td></tr><tr><td>Beetles (Coccinellidae)</td><td>Species</td><td>100</td><td>Roy and Brown (2018)</td></tr><tr><td rowspan="3">Pollinators</td><td>Parasitoids</td><td>Family</td><td>80</td><td>Yeo and Corbet (2015)</td></tr><tr><td>Bees (Anthophilla)</td><td>Genus or Species</td><td>95–100</td><td>Falk (2019)</td></tr><tr><td>Hoverflies (Syrphidae)</td><td>Genus or Species</td><td>90–95</td><td>Stubbs and Falk (2002)</td></tr></tbody></table>

## 3. Anticipated results and avenues for analysis

Our experiment will generate a large amount of quantitative data on soil structure, chemistry, and biology, on insect and bird biodiversity, and on cereal yield quantity and quality, across 25 sites in two UK landscapes. Although scientific evidence on the effects of regenerative practices on these metrics are scarce, especially relative to combinations of practices, we drew expectations from published literature to establish the experimental design (section 2.2) and to determine sampling sizes (section 2.8.1). Here, we present these expectations, and highlight avenues to analyse the experimental data that will be produced in this large, co-produced, landscape-scale experiment in commercial farms.

### 3.1. Expectations

#### 3.1.1. Soil physical, chemical and biological properties

We expect that long-term Regenerative fields will show improvements in soil physical, chemical, and biological properties compared to Conventional fields (table 3). Reduced soil disturbance using no-till techniques, combined with maintaining soil cover, is expected to increase soil organic carbon in the surface layers (Haddaway et al 2017; Ogle et al 2019) and promote biological activity of earthworms (Briones and Schmidt 2017). The increased use of N-fixing legumes in rotation will also increase chemical fertility (Sánchez-Navarro et al 2019). Incorporating soil residues, and integrating livestock is considered good practice under both conventional and regenerative systems (Khangura et al 2023), and can enhance soil carbon sequestration (Li et al 2019). Similarly, inclusion of cover crops, and diversifying crop rotations—often through the inclusion of spring crops—can enhance nitrogen and carbon storage in soil (Bai et al 2018, Abdalla et al 2019, Jian et al 2020). Conversely, soil compaction may initially increase with reduced tillage, due to a delay in biological activity recovery. However, it should decrease in the longer term via a diversity in rooting depth (Blanco-Canqui and Wortmann 2020), and the promotion of earthworm activity in soils receiving carbon inputs through living roots all year around, and experiencing reduced disturbance with less intensive tillage (Prendergast-Miller et al 2021).

However, the influence of no-till on soil carbon varies across climates (Ogle et al 2019, Abbas et al 2020, Sun et al 2020), between soil types (Page et al 2020, Shakoor et al 2021), and down the soil profile (Haddaway et al 2017). Our two farmer clusters differ in soil type and farming system characteristics, and this has implications for how soil characteristics may change in response to regenerative practices. For example, in
<caption>**Table 3.** The expected change in each of our measured response variables in regenerative fields compared to conventionally managed</caption>

fields. Variables may either increase (▲), decrease (▼) or be unaffected (■) relative to conventional sites—the direction of change is not indicative of the desirable state for that measure. Short term trends are those we expect to see in the duration of our study (< 4 years), long term trends may appear after >4 years of regenerative practices.

<table><thead><tr><th rowspan="2">Category</th><th rowspan="2">Outcome/Response</th><th colspan="2">Expected trend</th><th rowspan="2">References</th></tr><tr><th>Short term</th><th>Long term</th></tr></thead><tbody><tr><td rowspan="7">Soil properties</td><td>Water stable aggregates</td><td>▲</td><td>▲</td><td>Guest et al (2022)</td></tr><tr><td>Soil organic carbon</td><td>▲</td><td>▲</td><td>Haddaway et al (2017), Ogle et al (2019), Li et al (2019)</td></tr><tr><td>Bulk density</td><td>▲</td><td>▲</td><td>Li et al (2019)</td></tr><tr><td>Total N</td><td>▲</td><td>▲</td><td>García and Chacoff (2007), Garba et al (2022)</td></tr><tr><td>pH</td><td>▲</td><td>▲</td><td>Zhao et al (2022)</td></tr><tr><td>Earthworm density</td><td>▲</td><td>▲</td><td>Briones and Schmidt (2017), Bai et al (2018)</td></tr><tr><td>Biodiversity</td><td>Birds</td><td>▲ / ▲</td><td>▲</td><td>Donald et al (2002), Stoate et al (2003)</td></tr><tr><td></td><td>Pollinators</td><td>▲ / ▲</td><td>▲</td><td>Williams et al (2010), Roulston and Goodell, (2011), Ullman et al (2016), Antoine and Forrest (2021)</td></tr><tr><td></td><td>Beetles</td><td>▲ / ▲ / ▲</td><td>▲ / ▲ / ▲</td><td>Jowett et al (2021), Muller et al (2022)</td></tr><tr><td></td><td>Spiders</td><td>▲</td><td>▲ / ▲</td><td>Tahir et al (2012)</td></tr><tr><td></td><td>Parasitoids</td><td>▲</td><td>▲</td><td>Andow (1991)</td></tr><tr><td></td><td>Pests</td><td>▲ / ▲</td><td>▲</td><td>Kendall et al (1991), Holland et al (2004), Bryan et al (2021)</td></tr><tr><td rowspan="3">Grain yield and quality</td><td>Yield</td><td>▲</td><td>▲</td><td>Pittlekow et al (2015), Haung et al (2018), Pearsons et al (2022)</td></tr><tr><td>Quality</td><td>▲ / ▲</td><td>▲</td><td>Wozinak et al, (2014), Darguza and Gaile (2019), Montgomery et al (2022)</td></tr><tr><td>Inputs</td><td>▲ / ▲</td><td>▲</td><td>Khangura et al (2023)</td></tr></tbody></table>

the Eastern cluster, heavy clay soils are likely to have higher carbon storage than the sandy and shallow chalk-dominated soils of the Southwest (Page et al 2020). Similarly, nitrogen dynamics may differ across soil types, for example, minimum tillage can reduce nitrous oxide emissions in finer textured soils but tends to increase emissions on heavier soils in wet climates (Pelster et al 2023). Further, all farms in the Southwest are already mixed cropping systems (with integration of livestock), while this is not the case in the East.
<figure><img src="image_10.png" /></figure>

Therefore, we expect differences in the outcome of the regenerative practices between our two landscapes, and the Eastern cluster may show a more pronounced change based on their soil type and experimental introduction of livestock into the farming system.

Improvements in soil properties from a transition to regenerative agriculture are known to increase over time (Mondal and Chakraborty 2022). This may be the case not only for the Change fields, but also for the Regenerative fields in our experiment, which have transitioned to regenerative agriculture at different times and different speeds, using different combinations of practices (and not necessarily all nine practices yet), and may still be experiencing changes in practices and in soil physical and biological properties. Overall, we expect that, by the end of our experiment, the Change fields will achieve an intermediate outcome between Control and Regenerative fields, in relation to our indicators (figure 4). This means for some indicators, such as soil carbon, the short term changes (i.e. in the duration of our study) may not be visible (table 3).

### 3.1.2. Biodiversity

Overall, we anticipate that both abundance and diversity of biodiversity will respond positively to regenerative practices (table 3), through provision of an increased diversity of plant resources (Stoate et al 2003, Jaworski et al 2023b) and reducing disturbance on sensitive life stages, especially in the soil (Christmann 2022). For example, winter foraging birds benefit from seed-bearing cover crops (Stoate et al 2003), ground-nesting bees and some carabids may benefit from reduced tillage (Williams et al 2010, Roulston and Goodell 2011, Ullman et al 2016), and crop diversity at the landscape scale has been shown to increase pollinator diversity (Power et al 2016, Aguilera et al 2020, Raderschall et al 2021). Increased plant diversity in regenerative systems can also promote a diversity of natural enemies and therefore pest control services (Nicholls and Altieri 2013, Letourneau et al 2011; Aguilera et al 2020, Smith et al 2020, Jeavons et al 2023). Many other soil aspects, including chemistry, biology, and structure, relative to the farming systems
studied, may affect pollinators, although this remains understudied (Antoine and Forrest 2021, Carvalheiro et al 2021).

However, biodiversity responses may not be immediate, nor universal. For example, increased tillage intensity negatively impacts carabid abundance and diversity (Muller et al 2022), but several species show no response to tillage treatments and may be more abundant due to competitive advantage over species affected by particular tillage timings in breeding interruption, or preference for surface crop residues associated with no tillage (Baguette and Hance 1997, Blubaugh and Kaplan 2015, Jowett et al 2019, Jowett et al 2021). Similarly, spiders may not have strong responses to tillage (Tahir et al 2012), but may be positively impacted by cover crops or diverse plant cover along field boundaries (Sunderland and Samu 2000, Jaworski et al 2023b). As a result of increased predator activity, aphid abundances are expected to be lower in Regenerative fields, and parasitoid abundances are expected to strongly correlate to aphid abundances (resource concentration hypothesis; Andow 1991).

Other practices, such as the use of pesticides and adoption of agri-environment schemes may also impact biodiversity in Regenerative fields (figure 4(D)). For example, pest chemical pest management may have adverse effects on the persistence of natural enemies (Ruberson et al 1998), pollinators (Woodcock et al 2016b) and birds (Rigal et al 2023). Similarly, landscape composition and the presence of agri-environment schemes that enhance floral resources are also likely to impact pollinators (Sutter et al 2017, Jones and Rader 2022), natural enemies (Pywell et al 2015, Bullock et al 2021) and birds (Staggenborg and Anthes 2022). There may be interactive effects between regenerative practices and implementation of agri-environment schemes (Jaworski et al 2023b). For instance, the presence of semi-natural areas can enhance the effectiveness of in-field practices, such as the effects of crop diversity for promoting beetle and pollinator diversity (Aguilera et al 2020). While many regenerative farms reduce their use of insecticides, this is not a core principle of regenerative agriculture, making associated changes in biodiversity difficult to predict (Fenster et al 2021). We will collect detailed records of chemical inputs and agri-environment schemes from our experimental farms to control for their impact on our biodiversity outcomes (figure 4).

### 3.1.3. *Grain yield and quality*

There are expected trade-offs between biodiversity in agricultural fields and maintaining high yields and grain quality (Khangura et al 2023). However, the influence of tillage on crop yields varies depending on the crop, the climatic conditions and soil type (Pittlekow et al 2015, Haung et al 2018), and can be mediated by nutrient additions (Pittlekow et al 2015, Pearsons et al 2022), residue retention practices (Pittlekow et al 2015), and crop rotations (Wozinak et al 2014). For example, a meta-analysis by Pittlekow et al (2015) showed a marked decrease in yields within cereal and rice crops in response to no-till practices, but not legumes, oilseed or cotton. However, nutrient addition and crop residue retention narrowed the difference in crop yield between conventional and no-till management. Contrastingly, a meta-analysis by Huang et al (2018) showed a limited influence of tillage on barley yield, except in dry and alkaline soils where there was up to a 49% yield increase with adoption of no-till management.

The measurements of grain quality used by grain merchants are determined by its end use such as milling, baking, or brewing and comprise physical and chemical properties. Physical measurements include moisture levels, test (bushel) weight and grain damage. Chemical measurements include amount of protein, toxins, and malt extract for barley. Diversifying crop rotations may increase the suppression of weeds (Sharma et al 2021) and crop diseases (Khangura et al 2023). However, some farmers worry that grain damage from insects may increase under regenerative practices due to decreased pesticide inputs (Beacham et al 2023). Similarly increases in humid conditions under regenerative practices (Ogle et al 2019) may influence presence of disease and other toxins. Destruction of cover crops and management of pests and weeds is often performed chemically in regenerative agriculture, due to a reduction in mechanical (till-based) control methods (Khangura et al 2023). Herbicide use is also increasing across the UK (Pesticide Collaboration 2023). Therefore, we may expect to see agrochemical residues in grains regardless of treatment, especially in the short-term.

The influence of farming practices on nutrient content is highly varied, with some studies showing increases (Darguza and Gaile, 2019; Montgomery et al 2022), decreases (Wozinak, 2014) or no change in nutrient content (Pearsons et al 2022) compared to conventional practices. The nutrient content of crops is heavily influenced by climatic factors (Devita et al 2007, Wozinak, 2014), nutrient inputs (Pearsons et al 2022) and crop rotations (Lopez-Bellido et al 1998). However, these studies measure the influence of individual practices, and we believe that the combination of increased legumes in the rotation (Lopez-Bellido et al 1998), organic matter inputs (Pearsons et al 2022) and reduced tillage (Darguza and Gaile, 2019) in the Regenerative fields may result in an increase in micronutrients in crop yields. For example, Montgomery et al (2022) showed increases in vitamins (e.g. B1 and B2), minerals (Na and P), and phytochemicals (phenolics, phytosterols, and carotenoids) in regeneratively managed crops, with a similar combination of regenerative
practices, compared to conventional management. It is likely that crop nutrient content will respond rapidly to these changes, and with our detailed information on inputs, soil analysis and grain content we will also be able to track changes in nitrogen use efficiency across the transition as farms move towards reduced inputs.

## 3.2. Measuring the transition to regenerative agriculture

Despite many reported benefits of regenerative agriculture, adoption of a core combination of regenerative farming practices by UK farmers remains limited (Jaworski et al 2023a). The farmer interviews conducted in our experiment will provide an understanding of farmers' motivations for transitioning to regenerative farming, as well as the barriers to adoption. This farmers' perspective is fundamental to refining the definition of regenerative agricultural practices within a broader political landscape (e.g. comparisons with organic farming systems, and different approaches to certification; MacMillan and Benton 2014, Beacham et al 2023, Jaworski et al 2023a). Interviews with policymakers will complete the picture by providing an analysis of the volatile policy environment within which our research participants are following their uncertain path towards the adoption of regenerative agriculture practices.

Intensive practices, such as deep tillage and application of pesticides, may have long-lasting effects, even on land that has been recently converted to regenerative techniques (Crotty et al 2016, Beaumelle et al 2023). Regenerative farms in the experiment will have begun their regenerative journey at different times and may be at different points in the transition towards a fully regenerative agricultural system (as defined by our list of selected practices) (section 2.2). Similarly, the Change farms will be implementing different subsets of the nine practices. However, we expect that regenerative farms will show marked differences in soil properties and other biodiversity measures, compared to the Control fields (section 3.1). Over time, the change group is expected to become more like the regenerative group, as it adopts regenerative practices (figure 4(A)). This kind of time-series data can be one of the most effective measures of impacts of interventions in ecology (Wauchope et al 2021).

The transition to regenerative agriculture is inherently multifaceted, and understanding the changes resulting from the transition to regenerative agriculture requires analytical methods that consider the variation in implementation of practices both between and within farms. While previous studies have focused on the influence of single practices, the adaptability and flexibility in the implementation of different practices is one key strength of regenerative agriculture, but also of 'on farm experiments' through co-produced research (Lacoste et al 2022). Past research suggests that soil health and biodiversity outcomes can be influenced by the combination of practices, with some practices likely to act synergistically (e.g. transition to no-tillage after leys may be more effective in regenerating earthworm populations and soil aggregation than simply adopting no-tillage in continuous arable cropping). Others can be complementary, and reduce negative effects associated with a change of practice (e.g. nutrient addition reduces the yield loss related to no-till; Pittlekow et al (2015), but might also be achieved by direct drilling legumes as the first crop after leys). Finally, not all practice combinations are realistic to implement for farmers (Jaworski et al 2023a). Teasing out this nuance will be an important aspect of our research agenda (figure 5).

Therefore, an analysis of the impacts of regenerative practices must consider not just the presence of regenerative practices, but the combination of practices, the duration and the consistency with which they are implemented. One analytical method would be to generate a 'regenerative score' that can categorise the variation in practices and map the changes in implementation of practices over time (e.g. Fenster et al 2021, Jaworski et al 2023a). This score could then be used as an explanatory variable to measure the impact of a transition towards regenerative agriculture, rather than the initial categorical approach associated with the BACI design (section 2.2.). Similar to Jaworski et al (2023a), a regenerative score could be achieved by a point-based system that awards higher scores for the adoption of multiple regenerative practices that represent all five principles, with the most regenerative farms being those that implement all nine practices in our study (figure 4(B)). To better align the score with potential environmental benefits, it could also capture the quality of the practice (e.g. no tillage should be attributed a higher score than minimum tillage along the regenerative farming transition; Haddaway et al 2017), and the frequency of use of the practice, since real farming constraints may prevent the use of some practices in some years (Blanco-Canqui and Wortmann 2020), especially within the early transition period.

Alternatively, we could use multivariate distance-based ordination methods to compare the practices implemented for each site through time, and map the changes in a multidimensional space, composed of the farming practices, and possibly of other characteristics of the farming systems (e.g. soil type; figure 4(C)). The resulting distance-based matrix could then be compared to the outcomes using matrix-correlation methods (e.g. canonical correlation and multiple correspondence analyses; Adachi 2016). Advantages are the possibility to analyse all metrics simultaneously in a non-hierarchical way, but also to incorporate qualitative metrics (e.g. how successful a cover crop has been perceived to be by a farmer; Donaires et al 2023). This method may allow us to capture potential interactions between different types of regenerative practices, and
<figure><img src="image_11.png" /><figcaption>**Figure 5.** Theoretical interactions between implementation of regenerative practices ('P1' and 'P2') and their effect on the soil health outcomes. (A) Practices implemented together may act synergistically to produce significantly better outcomes than the sum of effects from individual practices (B). Alternatively, practices may be synergistic but there may be a limiting threshold that reduces the impact of additional practices, e.g. soil organic matter may be limited by the capacity of a given soil type (Page et al 2020). (C) Practices that have positive effects individually may also have antagonistic effects when implemented together. While this has been demonstrated for IPM (Stenberg 2017), there is no evidence of this for regenerative practices.</figcaption></figure>

between practice combinations and outcomes (figure 5). For example, Nunes *et al* (2018) found that benefits of no-till practices on soil health were enhanced with addition of cover crops, across a range of indicators, and we expect this to be further enhanced by introducing 2–3 year leys in arable rotations.

Finally, the relative effectiveness of in-field practices may be challenging to separate from changes occurring outside fields due to the adoption of agri-environment schemes (Scheper et al 2013). Farms from all three groups in our experiment have existing agreements or will adopt new agri-environment practices, alongside their transition to regenerative agriculture. The adoption of agri-environment schemes in areas out of production has been shown to have a larger impact on biodiversity than in-field schemes (Bátáry et al 2015), although this may vary depending on the local context, such as the proportion of semi-natural habitat areas (Jaworski et al 2023b). By characterising both in-field practices and semi-natural habitat management including agri-environment scheme practices, we can use multivariate modelling techniques to distinguish between the relative impact of in-field practices versus landscape-scale changes to habitat availability (figure 4(D)).

## 4. Discussion

We have presented the approach and methodology for our landscape-scale, transdisciplinary experiment aiming to quantify the benefits of regenerative agriculture for soil health, crop quality, and biodiversity, and to understand the experience of farmers in transitions to regenerative farming. Our experiment will bring novel scientific, quantitative evidence on the effects of regenerative agriculture on soil quality and carbon sequestration, crop yield quality, and bird and arthropod biodiversity in farmlands. By drawing from numerous disciplines, we will provide an integrative vision on the intrinsically complex nature of food system research (Holmes et al 2018, Kallio and Houtbeckers 2020, Jackson et al 2021).

The integrative co-production approach developed here is relevant to the adoption and spread of regenerative agriculture practices. This is because it starts with farmers’ definition of regenerative agriculture in two UK landscapes, and their efforts to constantly match scientific needs with applicable questions of interest to real-world farming. Secondly, the experiment builds on a large UK stakeholder network beyond the two farmer clusters, including the Agricultural Development and Advisory Service, the Agriculture and Horticulture Development Board, and the Soil Association, which will be key in disseminating results. The experiment should also drive farmers to seek scientific evidence of the outcomes of regenerative farming by themselves and adopt a scientific approach whenever useful to measure such outcomes. For instance, this experiment includes farmer workshops demonstrating simple, but scientifically informative soil tests that can be performed by farmers. This could help farmers connect with their soils and relate their farming practices to soil health, more than sending their soil samples to laboratories for analyses (Jaworski et al 2023a). No matter if scientific evidence confirms or disproves benefits of regenerative agriculture, the current co-production research will assess the outcome of regenerative agriculture practices, and this should help farmers define and optimize their implementation of regenerative agriculture practices in the UK and beyond. For instance, data collected will potentially help identify specific practices which should not be used in regenerative farming combinations, due to harmful environmental impact. Similarly, it could help identify
combinations of practices that bring the highest environmental benefits (Jaworski et al 2023a), and any potential trade-offs among these benefits, such as profitability, soil health and biodiversity.

This task is particularly urgent given contestations around the trajectory of UK agriculture. Until recently, UK agri-food policy has relied on ‘cheap’ (in terms of price) imports of food to focus more heavily on producing environmental ‘goods’ (i.e. ecosystem services) domestically rather than increasing agricultural productivity at all costs, a paradigm broadly characterised as post-productivist (Ward 1993, Beacham et al 2023). Yet the increased challenges associated with cross-border trade and the development of new UK agricultural policy post-Brexit has led to the notable resurgence of narratives around self-sufficiency whilst also maintaining the focus on environmental goods (Helm 2017, de Boon et al 2022, Beacham et al 2023, Defra 2023). In this context, regenerative agriculture has emerged as a favourable model for sustainable production ((Beacham et al 2023). The UK Department for Environment, Food and Rural Affairs (Defra) has developed ELMs as a tapered replacement to the European Union Common Agricultural Policy (CAP) in 2024, and SFI within ELMs aims to incentivise farmers to reach new goals partially aligned to regenerative farming, and notably sustainable soil management (Defra 2023). ELMs payments are planned to be based on management, not outcomes. Our experiment can help refine what practices are more likely to contribute to achieving environmental goals by providing evidence of the link between practices and outcomes. For instance, the scientific methodology adopted here to measure soil carbon sequestration could guide the selection of appropriate soil test requirements driving payments under the SFI.

Efforts to create a certification scheme for regenerative agriculture (e.g. A Greener World 2020) may not be desirable to farmers that value the flexibility of regenerative approaches (Beacham et al 2023), and may be hampered by logistic constraints, and similarity with other types of low-input approaches that have similar goals. However, political recognition of the links between practices and outcomes in our study will be an important step towards certification (Elrick et al 2022). Beyond certification, bringing traceability to the consumers along with guarantees of environmentally-friendly agriculture, new routes for recognition of the environmental benefits of regenerative agriculture have emerged. For instance, there is an emerging market of carbon credits, where farmers can receive income from carbon-emitting companies to protect their agricultural soil (Newton et al 2020, Jackson Hammond et al 2021). Here too, however, a regulatory framework is urgently required to ensure that carbon credits, and therefore companies’ environmental footprints, are associated with real, additional outcomes (i.e. the actual sequestration of soil carbon; and reduced net greenhouse gas fluxes e.g. of N₂O; Newton et al 2020, Jackson Hammond et al 2021).

Our landscape experiment originally aimed to follow a BACI design, which brings high statistical power by accounting for local environmental variability and specifically quantifying the changes in measured metrics through time (Osenberg et al 2006, Christie et al 2019). However, the co-production process resulted in a change of approach due to the logistical constraints of real farming (Lacoste et al 2022). This was due in part to the discrepancies in defining regenerative farming between clusters and farmers, which required moving away from clearly defined distinct treatments to focus on a more incremental, gradual, and multi-dimensional approach. Similarly, it was not ethically acceptable to ask farmers to transition to regenerative agriculture at the same pace and following the same trajectory, and indeed some of them had started transitioning before the baseline year (Year 1). While these changes may partially reduce statistical power, we advocate that similar approaches increasing the flexibility of ideal designs is necessary to produce scientific evidence strongly anchored in reality. Also, and for the same need to increase realism in scientific production (Gascuel-Odoux et al 2022), landscape-scale experiments are essential. This is true for their ecological dimension, where a number of ecosystem services are affected at spatial scales larger than the field (Rusch et al 2016, Harrison et al 2019, Jaworski et al 2022), and for which field-scale experiments cannot bring evidence on large scale spatio-temporal dynamics. This is also true for the social dimension, where only farmer networks can lead to the communication necessary for large-scale coordinated action. Such action can be useful to further enhance ecosystem services (e.g. landscape-scale crop mosaics promoting diverse communities of pests’ natural enemies; Jaworski et al 2022, 2023b), but is also absolutely crucial to induce food system changes and to make local outlets available (Meynard et al 2017, Thomine et al 2022).

Our experiment cannot directly quantify the effect of single practices on soil quality and biodiversity metrics, due to the systemic approach adopted here, and due to the large amount of social and environmental variation, although this is partially compensated by the relatively large scale (25 sites of 60 ha; Olson et al 2014, Alesso et al 2019). The large scale also prevents high precision on some metrics measured—for instance, soil properties are only measured twice—due to logistical constraints inherent to wide collaborative efforts. Similarly, the relatively short time scale of four years—determined by the duration of the majority of research funding in the UK—does not allow a complete measure of the transition to regenerative farming. Such transition likely occurs over a longer time in real farms, and benefits both for soil health and biodiversity may increase over a much longer time scale (Mondal and Chakraborty 2022). In addition, we could only measure a selection of soil, ecological and economic indicators, a trade-off with more precise and
complete measures at a smaller spatial scale and in a more controlled environment (controlling for crop type and rotation synchronisation, soil type, topography, climatic conditions, etc). Nonetheless, the scale and innovative approach of our experiment will allow us to develop more generalisable claims around the benefits of regenerative agriculture while also identifying some of the challenges that the farmers face. Further, the social scientific dimension of this research allows a better understanding of farmers’ perspectives on potential transitions to regenerative agriculture as they are formed within a wider constellation of social, political, economic and cultural conditions (Krzywoszynska 2019, Beacham et al 2023).

Our research collects unique, precise information on inputs used in farms included in our experiment; therefore, it has the potential to accurately measure the reliance on inputs and in particular pesticides, a key and flexible aspect of regenerative agriculture (Newton et al 2020, Schreefel et al 2020, Giller et al 2021). For instance, while some farmers consider the use of synthetic insecticides incompatible with regenerative farming, the use of synthetic herbicides remains common and may actually increase in regenerative farming to reduce weed infestation rather than through tilling practices, and to suppress leys and plant cover before drilling in the next crop (Beacham et al 2023, Jaworski et al 2023a). Our experiment can investigate the relationships between biodiversity decline and agrochemical use, a major driver of insect declines despite scarce evidence on long-term trends (Ewald et al 2015, 2016, Raven and Wagner 2021). Another major question that our experiment has the potential to answer is the relative contribution of landscape (composition and configuration of semi-natural habitats and of agricultural land) versus farming practices on biodiversity (McHugh et al 2022, Scheper et al 2013, Bátáry et al 2015). This is made possible since all ecological measures are accurately geolocated, and we work across a gradient of farming systems in two contrasted and heterogeneous UK landscapes, differing in the quality and quantity of semi-natural habitats, and in the use of agri-environment schemes.

Our approach aims to foster new transdisciplinary research integrating social and natural science dimensions in food system research and provide an evidence-based vision for informing beneficial changes in the field of agroecology from field-to landscape scales. Our research provides a demonstration of the benefits of co-production in experimental designs to measure and monitor change in real-world farming practices and ecology.

## Data availability statement

No new data were created or analysed in this study.

## Acknowledgments

We would like to thank the farmers of the H3 Consortium, whose input was invaluable in shaping the experimental design, aims and direction of the research. We would also like to thank the two anonymous reviewers whose comments greatly improved the clarity of this manuscript. This research study was supported by funding from the University of Cambridge, the University of Sheffield and UKRI Strategic Priorities Fund on Transforming UK Food Systems (H3 Project: BB/V004719/1).

## Author contribution statement

Funding: L V D, P J, D C.

Experimental design: C C J, K B, L V D, J R L, H3F C.

Farmers coordination: C C J, K B, H3F C.

Social science protocols: A K, P J, J D B.

Soil data protocols: J R L, T D, D C, L V D, H3F C, Y Z, P J W PW, N M M.

Ecological data protocols: C C J, L V D, N M M, L C, J H, N D, S H, K J.

Identification and lab work protocols: C C J, K B, L C, N M M, K J, L V D.

Theoretical development: K B, C C J, L V D

Figures: K B, C C J

Writing: C C J, K B

Manuscript editing: All.

## ORCID iDs

Katherine Berthon https://orcid.org/0000-0001-8224-2828

Coline C Jaworski https://orcid.org/0000-0002-6136-8656

Jonathan D Beacham https://orcid.org/0000-0001-8684-3719
Peter Jackson https://orcid.org/0000-0002-3654-1891

Jonathan Leake https://orcid.org/0000-0001-8364-7616

Niamh M McHugh https://orcid.org/0000-0001-6659-7266

Tim Daniell https://orcid.org/0000-0003-0435-4343

Anna Krzywoszynska https://orcid.org/0000-0002-8304-0440

Duncan Cameron https://orcid.org/0000-0002-5439-6544

John Holland https://orcid.org/0000-0003-3742-7035

Sue Hartley https://orcid.org/0000-0002-5117-687X

Kelly Jowett https://orcid.org/0000-0002-3672-7813

Lynn V Dicks https://orcid.org/0000-0002-8304-4468

## References

A Greener World 2020 (available at: https://agreenerworld.org.uk/certifications/certified-regenerative/) (Accessed 26 June 2023)

Abbas F, Hammad H M, Ishaq W, Farooque A A, Bakhat H F, Zia Z, Fahad S, Farhad W and Cerdà A 2020 A review of soil carbon dynamics resulting from agricultural practices *J. Environ. Manag.* **268** 110319

Abdalla M, Hastings A, Cheng K, Yue Q, Chadwick D, Espenberg M, Truu J, Rees R M and Smith P 2019 A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity *Glob. Change Biol.* **25** 2530–43

Adachi K 2016 Canonical correlation and multiple correspondence analyses *Matrix-Based Introduction to Multivariate Data Analysis* (Springer) (https://doi.org/10.1007/978-981-10-2341-5_14)

Aguilera G et al 2020 Crop diversity benefits carabid and pollinator communities in landscapes with semi-natural habitats *J. Appl. Ecol.* **57** 2170–9

AHDB Wheat Growth Guide 2021 (available at: https://ahdb.org.uk/knowledge-library/wheat-growth-guide)

Alesso C A, Cipriotti P A, Bollero G A and Martin N F 2019 Experimental designs and estimation methods for on-farm research: a simulation study of corn yields at field scale *Agron. J.* **111** 2724–35

Andow D A 1991 Vegetational diversity and arthropod population response *Annu. Rev. Entomol.* **36** 561–86

Antoine C M and Forrest J R 2021 Nesting habitat of ground-nesting bees: a review *Ecol. Entomol.* **46** 143–59

Atkinson P W, Fuller R A, Gillings S and Vickery J A 2006 Counting birds on farmland habitats in winter *Bird Study* **53** 303–9

Baguette M and Hance T H 1997 Carabid beetles and agricultural practices: influence of soil ploughing *Biol. Agric. Hortic.* **15** 185–90

Bai Z, Caspari T, Gonzalez M R, Batjes N H, Mäder P, Bünemann E K and Tóth Z 2018 Effects of agricultural management practices on soil quality: a review of long-term experiments for Europe and China *Agric. Ecosyst. Environ.* **265** 1–7

Batáry P, Dicks L V, Kleijn D and Sutherland W J 2015 The role of agri-environment schemes in conservation and environmental management *Conserv. Biol.* **29** 1006–16

Bayer Aphid Expert Guide 2013 (available at: https://cropscience.bayer.co.uk/blog/articles/2013/05/aphid-expert-guide-published/)

Beacham J D, Jackson P, Jaworski C C, Krzywoszynska A and Dicks L V 2023 Contextualising farmer perspectives on regenerative agriculture: a post-productivist future? *J. Rural Studies* **102** 103100

Beaumelle L, Tison L, Eisenhauer N, Hines J, Malladi S, Pelosi C, Thouvenot L and Phillips H R 2023 Pesticide effects on soil fauna communities—A meta-analysis *J. Appl. Ecol.* **60** 1239–53

Blanco-Canqui H and Wortmann C S 2020 Does occasional tillage undo the ecosystem services gained with no-till? A review *Soil Till Res.* **198** 104534

Bless A, Davila F and Plant R 2023 A genealogy of sustainable agriculture narratives: implications for the transformative potential of regenerative agriculture *Agric. Hum. Values* **40** 1379–97

Blubaugh C K and Kaplan I 2015 Tillage compromises weed seed predator activity across developmental stages *Biol. Control* **81** 76–82

Breeze T D et al 2021 Pollinator monitoring more than pays for itself *J. Appl. Ecol.* **58** 44–57

Briones M Jesús and Schmidt O 2017 Conventional tillage decreases the abundance and biomass of earthworms and alters their community structure in a global meta-analysis *Glob. Change Biol.* **23** 4396–419

Bryan C J, Sipes S D, Arduser M, Kassim L, Gibson D J, Scott D A and Gage K L 2021 Efficacy of cover crops for pollinator habitat provision and weed suppression *Environ. Entomol.* **50** 208–21

Bullock J M, McCracken M E, Bowes M J, Chapman R E, Graves A R, Hinsley S A and Pywell R F 2021 Does agri-environmental management enhance biodiversity and multiple ecosystem services?: a farm-scale experiment *Agric. Ecosyst. Environ.* **320** 107582

Carvalheiro L G, Bartomeus I, Rollin O, Timóteo S and Tinoco C F 2021 The role of soils on pollination and seed dispersal *Phil. Trans. Roy. Soc. B* **376** 20200171

Chabert A and Sarthou J-P 2017 Practices of conservation agriculture prevail over cropping systems and landscape heterogeneity in understanding the ecosystem service of aphid biocontrol *Agric. Ecosyst. Environ.* **249** 70–79

Chabert A and Sarthou J-P 2020 Conservation agriculture as a promising trade-off between conventional and organic agriculture in bundling ecosystem services *Agric. Ecosyst. Environ.* **292** 106815

Chaplin-Kramer R, de Valpine P, Mills N J and Kremen C 2013 Detecting pest control services across spatial and temporal scales *Agric. Ecosyst. Environ.* **181** 206–12

Christie A P, Amano T, Martin P A, Shackelford G E, Simmons B I and Sutherland W J 2019 Simple study designs in ecology produce inaccurate estimates of biodiversity responses *J. Appl. Ecol.* **56** 2742–54

Christmann S 2022 Regard and protect ground-nesting pollinators as part of soil biodiversity *Ecol. Appl.* **32** e2564

Ciss M, Parisey N, Moreau F, Dedryver C-A and Pierre J-B 2014 A spatiotemporal model for predicting grain aphid population dynamics and optimizing insecticide sprays at the scale of continental France *Environ. Sci. Pollut. Res.* **21** 819–4827

Colbach N, Dessaint F and Forcella F 2000 Evaluating field-scale sampling methods for the estimation of mean plant densities of weeds *Weed Res. Oxf.* **40** 411–30

Crotty F V, Fychan R, Sanderson R, Rhymes J R, Bourdin F, Scullion J and Marley C L 2016 Understanding the legacy effect of previous forage crop and tillage management on soil biology, after conversion to an arable crop rotation *Soil Biol. Biochem.* **103** 241–52

Darguza M and Gaile Z 2019 Yield and quality of winter wheat, depending on crop rotation and soil tillage *Res. Rural Dev.* **2** 29–35

de Boon A, Sandström C and Rose D C 2022 Perceived legitimacy of agricultural transitions and implications for governance. Lessons learned from England’s post-Brexit agricultural transition *Land Use Policy* **116** 106067
Defra 2022 Chapter 7: crops Summary (available at: www.gov.uk/government/statistics/agriculture-in-the-united-kingdom-2021/chapter-7-crops.)

Defra 2023 Environmental Land Management (ELM) update: how government will pay for land-based environment and climate goods and services (available at: www.gov.uk/government/publications/environmental-land-management-update-how-government-will-pay-for-land-based-environment-and-climate-goods-and-services/environmental-land-management-elm-update-how-government-will-pay-for-land-based-environment-and-climate-goods-and-services)

Devita P, Dipaolo E, Fecondo G, Difonzo N and Pisante M 2007 No-tillage and conventional tillage effects on durum wheat yield, grain quality and soil moisture content in southern Italy Soil Tillage Res. 92 69–78

Doherty B *et al* 2022 Transformations to regenerative food systems—An outline of the FixOurFood project Nutr. Bull. 47 106–14

Donaires O S, Cezarino L O, Liboni L B, Ribeiro E M S and Martins F P 2023 Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research Qual. Quant. 57 1–30

Donald P F, Pisano G, Rayment M D and Pain D J 2002 The Common Agricultural Policy, EU enlargement and the conservation of Europe's farmland birds Agric. Ecosyst. Environ. 89 167–82

Elmqvist T *et al* 2022 EASAC policy report 44: Regenerative agriculture in Europe: A critical analysis of contributors to European Union Farm to Fork and Biodiversity Strategies

Elrick W, Luke H and Stimpson K 2022 Exploring opportunities and constraints of a certification scheme for regenerative agricultural practice Agroecol. Sustain. Food Syst. 46 1527–49

Evans D L, Quinton J N, Davies J A, Zhao J and Govers G 2020 Soil lifespans and how they can be extended by land use and management change Environ. Res. Lett. 15 0940b2

Ewald J A, Wheatley C J, Aebischer N J, Duffield S J and Heaver D 2016 Investigation of the impact of changes in pesticide use on invertebrate populations. Natural England Commissioned Report NECR182 (Natural England)

Ewald J A, Wheatley C J, Aebischer N J, Moreby S J, Duffield S J, Crick H Q P and Morecroft M B 2015 Influences of extreme weather, climate and pesticide use on invertebrates in cereal fields over 42 years Glob. Change Biol. 21 3931–50

Falk S 2019 Field guide to the bees of Great Britain and Ireland (Bloomsbury Publishing)

Farm of the Future: Journey to Net Zero 2022 Report from the Royal Agriculture Society of England (available at: www.rase.org.uk/reports)

Fenster T L D, LaCanne C E, Pecenka J R, Schmid R B, Bredeson M M, Busenitz K M, Michels A M, Welch K D and Lundgren J G 2021 Defining and validating regenerative farm systems using a composite of ranked agricultural practices F1000 Res. 10 115

Game and Wildlife Conservation Trust 2019 Farmer Clusters: landscape-scale, farmer-led conservation projects and groups (available at: www.farmerclusters.com/)

Garba I I, Bell L W and Williams A 2022 Cover crop legacy impacts on soil water and nitrogen dynamics, and on subsequent crop yields in drylands: a meta-analysis Agron. Sustain. Dev. 42 34

García D and Chacoff N P 2007 Scale-dependent effects of habitat fragmentation on hawthorn pollination, frugivory, and seed predation Conserv. Biol. 21 400–11

Gascuel-Odoux C, Lescourret F, Dedieu B, Detang-Dessendre C, Faverdin P, Hzard L and Caquet T 2022 A research agenda for scaling up agroecology in European countries Agron. Sustain. Dev. 42 53

Giller K E, Andersson J A, Corbeels M, Kirkegaard J, Mortensen D, Erenstein O and Vanlauwe B 2015 Beyond conservation agriculture Front. Plant Sci. 6 870

Giller K E, Hijbeek R, Andersson J A and Sumberg J 2021 Regenerative agriculture: an agronomic perspective Outlook Agric. 50 13–25

Golafshan M G and Yasari E 2012 Comparison of sampling methods for estimating seed bank and weed population densities during the growing season J. Agric. Sci. 4 39

Gordon E, Davila F and Riedy C 2022 Transforming landscapes and mindscapes through regenerative agriculture Agric. Hum. Values 39 809–26

Gordon E, Davila F and Riedy C 2023 Regenerative agriculture: a potentially transformative storyline shared by nine discourses Sustain. Sci. 1–17

Gough C and Shackley S 2001 The respectable politics of climate change: the epistemic communities and NGOs Int. Affairs 77 329–46

Graves A R, Morris J, Deeks L K, Rickson R J, Kibblewhite M G, Harris J A, Farewell T S and Truckle I 2015 The total costs of soil degradation in England and Wales Ecol. Econ. 119 399–413

Guest E J, Palfreeman L J, Holden J, Chapman P J, Firbank L G, Lappage M G, Helgason T and Leake J R 2022 Soil macroaggregation drives sequestration of organic carbon and nitrogen with three-year grass-clover leys in arable rotations Sci. Tot. Environ. 852 158358

Haddaway N R, Hedlund K, Jackson L E, Kätterer T, Lugato E, Thomsen I K and Isberg P E 2017 How does tillage intensity affect soil organic carbon? A systematic review Environ. Evid. 6 1–48

Hallam J *et al* 2020 Effect of earthworms on soil physico-hydraulic and chemical properties, herbage production, and wheat growth on arable land converted to ley Sci. Total Environ. 713 136491

Harrison T, Gibbs J and Winfree R 2019 Anthropogenic landscapes support fewer rare bee species Landsc. Ecol. 34 967–78

Helm D 2017 Agriculture after Brexit Oxford Rev. Econ. Policy 33 S124–33

Hobbs P R, Sayre K and Gupta R 2008 The role of conservation agriculture in sustainable agriculture Phil. Trans. Roy. Soc. B 363 543–55

Hohbein R R and Conway C J 2018 Pitfall traps: a review of methods for estimating arthropod abundance Wildl. Soc. Bull. 42 597–606

Holland J M, Frampton G K, Gilci T and Wratten S D 1994 Arable acronyms analysed—a review of integrated farming systems research in Western Europe Ann. Appl. Biol. 125 399–438

Holland J M, Hutchison M A S, Smith B and Aebischer N J 2006 A review of invertebrates and seed-bearing plants as food for farmland birds in Europe Ann. Appl. Biol. 148 49–71

Holland J M, McHugh N M and Salinari F 2021 Field specific monitoring of cereal yellow dwarf virus aphid vectors and factors influencing their immigration within fields Pest Manage. Sci. 77 4100–8

Holland J M, Thomas C F G, Birkett T, Southway S and Oaten H 2005 Farm-scale spatiotemporal dynamics of predatory beetles in arable crops J. Appl. Ecol. 42 1140–52

Holland J M, Winder L, Woolley C, Alexander C J and Perry J N 2004 The spatial dynamics of crop and ground active predatory arthropods and their aphid prey in winter wheat Bull. Entomol. Res. 94 419–31

Holmes H, Gregson N, Watson M, Buckley A, Chiles P, Krzywoszynska A and Maywin J 2018 Interdisciplinarity in transdisciplinary projects: circulating knowledges, practices and effects disP-The Plan. Rev. 54 77–93

Huang Y, Ren W, Wang L, Hui D, Grove J H, Yang X, Tao B and Goff B 2018 Greenhouse gas emissions and crop yield in no-tillage systems: A meta-analysis Agric. Ecosyst. Environ. 268 144–53
Hubbard C E 1992 *Grasses: A Guide to their Structure, Identification, Uses, and Distribution in the British Isles* 3rd edn (Penguin Books) pp 461

Isaacs R, Tuell J, Fiedler A, Gardiner M and Landis D 2009 Maximizing arthropod-mediated ecosystem services in agricultural landscapes: the role of native plants *Front. Ecol. Environ.* **7** 196–203

Jackson Hammond A A, Motew M, Brummitt C D, DuBuisson M L, Pinjuv G, Harburg D V and Kumar A A 2021 Implementing the soil enrichment protocol at scale: opportunities for an agricultural carbon market *Front. Clim.* **64** 686440

Jackson P *et al* 2021 Healthy soil, healthy food, healthy people: an outline of the H3 project *Nutr. Bull.* **46** 497–505

Jacobs J H, Clark S J, Denholm I, Goulson D, Stoate C and Osborne J L 2009 Pollination biology of fruit-bearing hedgerow plants and the role of flower-visiting insects in fruit-set *Ann. Bot.* **104** 1397–404

Jaworski C C, Chailleux A, Bearez P and Desneux N 2015 Apparent competition between major pests reduces pest population densities on tomato crop, but not yield loss *J. Pest Sci.* **88** 793–803

Jaworski C C, Krzywoszynska A, Leake J R and Dicks L V 2023a Sustainable soil management in the UK: a survey of current practices and how they relate to the principles of regenerative agriculture *Soil Use Manage.* **40** e12908

Jaworski C C, Thomine E, Rusch A, Lavoir A-V, Wang S and Desneux N 2023b Crop diversification to promote arthropod pest management: a review *Agric. Comm* **1** 100004

Jaworski C C, Thomine E, Rusch A, Lavoir A-V, Xiu C, Ning D, Lu Y, Wang S and Desneux N 2022 At which spatial scale does crop diversity enhance natural enemy populations and pest control? An experiment in a mosaic cropping system *Agronomy* **12** 1973

Jeavons E, Le Lann C and van Baaren J 2023 Interactions among beneficial arthropods: combining ecological theory with agroecological management *Entomol. Gen.* **43** 243–59

Jian J, Du X, Reiter M S and Stewart R D 2020 A meta-analysis of global cropland soil carbon changes due to cover cropping *Soil Biol. Biochem.* **143** 107735

Jones J and Rader R 2022 Pollinator nutrition and its role in merging the dual objectives of pollinator health and optimal crop production *Phil. Trans. R. Soc. B* **377** 20210170

Jones-Walters L 1989 *Keys to the Families of British Spiders (AIDGAP) (Field Studies)* 9 (Field Studies Council) pp 443

Jowett K 2022 *Farmland Carabids Identification Guide* (Rothamsted Research)

Jowett K, Milne A E, Garrett D, Potts S G, Senapathi D and Storkey J 2021 Above- and below-ground assessment of carabid community responses to crop type and tillage *Agric. For. Entomol.* **23** 1–12

Jowett K, Milne A E, Metcalfe H, Hassall K L, Potts S G, Senapathi D and Storkey J 2019 Species matter when considering landscape effects on carabid distributions *Agric. Ecosyst. Environ.* **285** 106631

Kallio G and Houtbeckers E 2020 Academic knowledge production: framework of practical activity in the context of transformative food studies *Front. Sustain. Food Syst.* **4** 577351

Kendall D A, Chinn N E, Smith B D, Tidboald C, Winstone L and Western N M 1991 Effects of straw disposal and tillage on spread of barley yellow dwarf virus in winter barley *Ann. Appl. Biol.* **119** 359–64

Kennedy T F, McDonald J G, Connery J and Purvis G 2010 A comparison of the occurrence of aphids and barley yellow dwarf virus in minimum-till and conventional-till autumn-sown cereals *J. Agric. Sci.* **148** 407–19

Khangura R, Ferris D, Wagg C and Bowyer J 2023 Regenerative Agriculture—A Literature Review on the Practices and Mechanisms Used to Improve Soil Health *Sustainability* **15** 2338

Kleijn D, Bommarco R, Fijen T P M, Garibaldi L A, Potts S G and van der Putten W H 2019 Ecological Intensification: bridging the Gap between Science and Practice *Trends Ecol. Evol.* **34** 154–66

Kosewska A, Skalski T and Nietupski M 2014 Effect of conventional and non-inversion tillage systems on the abundance and some life history traits of carabid beetles (Coleoptera: carabidae) in winter triticale fields *Eur. J. Entomol* **111** 669–76

Krzywoszynska A 2019 Making knowledge and meaning in communities of practice: what role may science play? The case of sustainable soil management in England *Soil Use Manage.* **35** 160–8

LaCanne C E and Lundgren J G 2018 Regenerative agriculture: merging farming and natural resource conservation profitably *Peer J.* **6** e4428

Lacoste M, Cook S, McNee M, Gale D, Ingram J and Hall A 2022 On-Farm Experimentation to transform global agriculture *Nat. Food* **3** 11–18

Lal R, Reicosky D and Hanson J 2007 Evolution of the plow over 10,000 years and the rationale for no-till farming *Soil Till Res.* **93** 1–12

Legrand M A, Colinet H, Vernon P and Hance T 2004 Autumn, winter and spring dynamics of aphid *Sitobion avenae* and parasitoid *Aphidius rhopalosiphii* interactions *Ann. Appl. Biol.* **145** 139–44

Letourneau D K *et al* 2011 Does plant diversity benefit agroecosystems? A synthetic review *Ecol. Appl.* **21** 9–21

Li Y, Li Z, Cui S, Jagadamma S and Zhang Q 2019 Residue retention and minimum tillage improve physical environment of the soil in croplands: a global meta-analysis *Soil Tillage Res.* **194** 104292

Li Y, Song D, Liang S, Dang P, Qin X, Liao Y and Siddique K H, 2020 Effect of no-tillage on soil bacterial and fungal community diversity: a meta-analysis *Soil Tillage Res.* **204** 104721

Liptzin D, Norris C E, Cappellazzi S B, Mac Bean G, Cope M, Greub K L and Honeycutt C W 2022 An evaluation of carbon indicators of soil health in long-term agricultural experiments *Soil Biol. Biochem.* **172** 108708

López-Bellido L, Fuentes M, Castillo J and López-Garrido F 1998 Effects of tillage, crop rotation and nitrogen fertilization on wheat-grain quality grown under rainfed Mediterranean conditions *Field Crops Res.* **57** 265–76

Lövei G L and Sunderland K D 1996 Ecology and behavior of ground beetles (Coleoptera: carabidae) *Annu. Rev. Entomol.* **41** 231–56

Luff M L 2007 *The Carabidae (ground beetles) of Britain and Ireland* 2nd edn (Handbooks for the Identification of British Insects) Vol 5 (Royal Entomological Society)

MacMillan T and Benton T 2014 Agriculture: engage farmers in research *Nature* **509** 25–27

McCray K W 2018 A review of sampling and monitoring methods for beneficial arthropods in agroecosystems *Insects* **9** 170

McHugh N M, McVeigh A, Bown B, Powell R, Wilson P and Holland J M 2022 The value of two agri-environment scheme habitats for pollinators: annually cultivated and floristically enhanced grass margins *Agric. Ecosyst. Environ.* **326** 107773

Meynard J M, Jeuffroy M H, Bail M L, Lefèvre A, Magrini M B and Michon C 2017 Designing coupled innovations for the sustainability transition of agrifood systems *Agric. Syst.* **157** 330–9

Miller-Klugesherz J A and Sanderson M R 2023 Good for the soil, but good for the farmer? Addiction and recovery in transitions to regenerative agriculture *J. Rural Stud.* **103** 103123

Mondal S and Chakraborty D 2022 Global meta-analysis suggests that no-tillage favourably changes soil structure and porosity *Geoderma* **405** 115443
Montgomery D R, Biklé A, Archuleta R, Brown P and Jordan J 2022 Soil health and nutrient density: preliminary comparison of regenerative and conventional farming *PeerJ* 10 e12848

Monticelli L S, Nguyen L T H, Amiens-Desneux E, Luo C, Lavoir A-V, Gatti J-L and Desneux N 2019 The preference-performance relationship as a means of classifying parasitoids according to their specialization degree *Evol. Appl.* 12 1626–40

Müller P, Neuhoff D, Nabel M, Schiffers K and Döring T F 2022 Tillage effects on ground beetles in temperate climates: a review *Agron. Sustain. Dev.* 42

Newton P, Civita N, Frankel-Goldwater L, Bartel K and Johns C 2020 What is regenerative agriculture? A review of scholar and practitioner definitions based on processes and outcomes *Front. Sustain. Food Syst.* 26 194

Nicholls C I and Altieri M A 2013 Plant biodiversity enhances bees and other insect pollinators in agroecosystems. A review *Agron. Sustain. Dev.* 33 257–74

Norström A V et al 2020 Principles for knowledge co-production in sustainability research *Nat. Sustain.* 3 182–90

Nunes M R, van Es H M, Schindelbeck R, Ristow A J and Ryan M 2018 No-till and cropping system diversification improve soil health and crop yield *Geoderma* 328 30–43

O'Connor R S, Kunin W E, Garratt M P D, Potts S G, Roy H E and Carvell C 2019 Monitoring insect pollinators and flower visitation: the effectiveness and feasibility of different survey methods *Methods Ecol. Evol.* 10 2129–40

O'Donoghue T, Minasny B and McBratney A 2022 Regenerative agriculture and its potential to improve Farmscape function *Sustainability* 14 5815

Ogle S M, Alsaker C, Baldock J, Bernoux M, Breidt F J, McConkey B, Regina K and Vazquez-Amabile G G 2019 Climate and soil characteristics determine where no-till management can store carbon in soils and mitigate greenhouse gas emissions *Sci Rep* 9

Olson K R, Al-Kaisi M M, Lal R and Lowery B 2014 Experimental consideration, treatments, and methods in determining soil organic carbon sequestration rates *Soil Sci. Soc. Am. J.* 78 348–60

Osenberg C W, Bolker B M, White J S S, St Mary C M and Shima J S 2006 Statistical issues and study design in ecological restorations: lessons learned from marine reserves *Foundations of Restoration Ecology* ed A D Falk, M A Palmer and J B Zedler (Island Press) pp 280–302 (available at: https://books.google.fr/books?id=5iUbP6lfYjgC)

Page K L, Dang Y P, Menzies N W and Dalal R C 2020 No-Till systems to sequester soil carbon: potential and reality *No-till Farming Systems for Sustainable Agriculture* ed Y Dang, R Dalal and N Menzies (Springer) (https://doi.org/10.1007/978-3-030-46409-7_18)

Pan H, Liu B, Jaworski C C, Yang L, Liu Y, Desneux N, Thomine E and Lu Y 2020 Effects of aphid density and plant taxa on predatory ladybeetle abundance at field and landscape scales *Insects* 11 695

Pearsons K A, Omondi E C, Heins B J, Zinati G, Smith A and Rui Y 2022 Reducing tillage affects long-term yields but not grain quality of maize, soybeans, oats, and wheat produced in three contrasting farming systems *Sustainability* 14 631

Pelster D E, Matteau J P, Farrell R and Hernandez Ramirez G 2023 Tillage effects on growing season nitrous oxide emissions in Canadian cropland soils *Can. J. Soil Sci.* 104 1–10

Pittelkow C M, Linquist B A, Lundy M E, Liang X, van Groenigen K J, Lee J, van Gestel N, Six J, Venterea R T and van Kessel C 2015 When does no-till yield more? A global meta-analysis *Field Crops Res.* 183 156–68

POMS 2023 UK Pollinator Monitoring Scheme: flower-Insect timed count guidance Version 6 (available at: https://www.google.com/url?q=https://ukpoms.org.uk/sites/default/files/pdf/FIT%2520Count%2520survey%2520guidance%2520v6.pdf&sa=D&source=docs&ust=1689783843187603&usg=AovVaw0a5bs4rj2y8Segf8u7TDE1)

Power E F, Jackson Z and Stout J C 2016 Organic farming and landscape factors affect abundance and richness of hoverflies (Diptera, Syrphidae) in grasslands *Insect Conserv. Divers.* 9 244–53

Prendergast-Miller M T et al 2021 Arable fields as potential reservoirs of biodiversity: Earthworm populations increase in new leys *Sci. Total Environ.* 789 147880

Puerta V L, Pujol Pereira E I, Wittwer R, van der Heijden M and Six J 2018 Improvement of soil structure through organic crop management, conservation tillage and grass-clover ley *Soil Tillage Res.* 180 1–9

Pywell R F, Heard M S, Woodcock B A, Hinsley S, Ridding L, Nowakowski M and Bullock J M 2015 Wildlife-friendly farming increases crop yield: evidence for ecological intensification *Proc. R. Soc. B* 282 20151740

R Core Team 2022 R: a language and environment for statistical computing. R foundation for statistical computing (available at: www.R-project.org/)

Raderschall C A, Bommarco R, Lindström S A and Lundin O 2021 Landscape crop diversity and semi-natural habitat affect crop pollinators, pollination benefit and yield *Agric. Ecosyst. Environ.* 306 107189

Raven P H and Wagner D L 2021 Agricultural intensification and climate change are rapidly decreasing insect biodiversity *Proc. Natl Acad. Sci.* 118 e2002548117

Raymond L, Sarthou J-P, Plantegenest M, Gauffre B, Ladet S and Vialatte A 2014 Immature hoverflies overwinter in cultivated fields and may significantly control aphid populations in autumn *Agric. Ecosyst. Environ.* 185 99–105

Rigal S 2023 Farmland practices are driving bird population decline across Europe *Proc. of the National Academy of Sciences* vol 120 p e2216573120

Ritz K 2021 *The Groundswell 5 Principles and Soil Sense* (available at: https://groundswellag.com/karl-ritz-the-groundswell-5-principles-and-soil-sense/)

Robinson R A and Sutherland W J 2002 Post-war changes in arable farming and biodiversity in Great Britain *J. Appl. Ecol.* 39 157–76

Rodale R 1983 Breaking new ground: the search for a sustainable agriculture *Futurist* 1 15–20 (available at: https://eric.ed.gov/?id=EJ275343)

Römbke J, Sousa J P, Schouten T and Riepert F 2006 Monitoring of soil organisms: a set of standardized field methods proposed by ISO *Eur. J. Soil Biol.* 42 S61–S64

Rose F 2006 *The Wild Flower Key - How to identify wild flowers, trees and shrubs in Britain and Ireland* (Frederick Warne & Co. Ltd.) pp 480

Roulston T H and Goodell K 2011 The role of resources and risks in regulating wild bee populations *Annu. Rev. Entomol.* 56 293–312

Roy H and Brown P 2018 *Field guide to the ladybirds of Great Britain and Ireland* (Bloomsbury Publishing)

Ruberson J, Nemoto H and Hirose Y 1998 Pesticides and conservation of natural enemies in pest management *Conservation Biological Control* (Academic) pp 207–20

Rusch A et al 2016 Agricultural landscape simplification reduces natural pest control: a quantitative synthesis *Agric. Ecosyst. Environ.* 221 198–204

Sánchez-Navarro V, Zornoza R, Faz Á and Fernández J A 2019 Comparing legumes for use in multiple cropping to enhance soil organic carbon, soil fertility, aggregates stability and vegetables yields under semi-arid conditions *Sci. Hortic.* 246 835–41
Scheper J, Holzschuh A, Kuussaari M, Potts S G, Rundlöf M, Smith H G, Kleijn D and Gomez J 2013 Environmental factors driving the effectiveness of European agri-environmental measures in mitigating pollinator loss – a meta-analysis *Ecol. Lett.* **16** 912–20

Schreefel L, Schulte R P O, De Boer I J M, Schrijver A P and Van Zanten H H E 2020 Regenerative agriculture—the soil is the base *Glob. Food Secur.* **26** 100404

Seymour M and Connelly S 2023 Regenerative agriculture and a more-than-human ethic of care: a relational approach to understanding transformation *Agric. Hum. Values* **40** 231–44

Shakoor A, Shahbaz M, Farooq T H, Sahar N E, Shahzad S M, Altaf M M and Ashraf M 2021 A global meta-analysis of greenhouse gases emission and crop yield under no-tillage as compared to conventional tillage *Sci. Total Environ.* **750** 142299

Sharma G, Shrestha S, Kunwar S and Tseng T 2021 Crop diversification for improved weed management: a review *Agriculture* **11** 461

Sherwood S and Uphoff N 2000 Soil health: research, practice and policy for a more regenerative agriculture *Appl. Soil Ecol.* **15** 85–97

Sloggett J J 2008 Weighty matters: body size, diet and specialization in aphidophagous ladybird beetles (Coleoptera: coccinellidae Europ J. Entomol. **105** 381–9 (available at: www.eje.cz/scripts/viewabstract.php?abstract=1341)

Smith B, Aebischer N J, Ewald J, Moreby S, Potter C and Holland J M 2020 The potential of arable weeds to reversing invertebrate declines and associated ecosystem services in cereal crops *Front. Sustain. Food Syst.* **3** 118

Staggenborg J and Anthes N 2022 Long-term fallows rate best among agri-environment scheme effects on farmland birds—A meta-analysis *Conserv. Lett.* **15** e12904

Stenberg J A 2017 A conceptual framework for integrated pest management *Trends Plant Sci* **22** 759–69

Stephenson A G 1981 Flower and Fruit: proximate causes and ultimate functions *Annu. Rev. Ecol. Syst.* **12** 253–79

Stoate C, Szczur J and Aebischer N J 2003 Winter use of wild bird cover crops by passerines on farmland in northeast England *Bird Study* **50** 15–21

Stubbs A E and Falk S J 2002 British Entomological Society *British Hoverflies: An illustrated identification guide* 2nd edn (Dorset Press) pp 469

Sun W, Canadell J G, Yu L, Zhang W, Smith P, Fischer T and Huang Y 2020 Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture *Glob. Change Biol.* **26** 3325–35

Sunderland K and Samu F 2000 Effects of agricultural diversification on the abundance, distribution, and pest control potential of spiders: a review *Entomol. Exp. Appl.* **95** 1–13

Sutter L, Jeanneret P, Bartual A M, Bocci G and Albrecht M 2017 Enhancing plant diversity in agricultural landscapes promotes both rare bees and dominant crop-pollinating bees through complementary increase in key floral resources *J. Appl. Ecol.* **54** 1856–64

Tahir H M, la Butt A, Khan S Y, Bhatti M F and Mukhtar M K 2012 Effect of tillage practice on the seasonal dynamics of ground spiders *Pak. J. Zool.* **44**

Tamburini G, De Simone S, Sigura M, Boscutti F and Marini L 2016 Conservation tillage mitigates the negative effect of landscape simplification on biological control *J. Appl. Ecol.* **53** 233–41

The Pesticide Collaboration 2023 *Pesticides and the Climate Crisis: A Vicious Cycle* Pesticide Action Network UK (available at: https://pesticidecollaboration.org/wp-content/uploads/2023/07/Pesticides-and-the-Climate-Crisis-2.pdf)

Thomine E, Mumford J, Rusch A and Desneux N 2022 Using crop diversity to lower pesticide use: socio-ecological approaches *Sci. Tot. Environ.* **804** 150156

Tillman G, Schomberg H, Phatak S, Mullinix B, Lachnicht S, Timper P and Olson D 2004 Influence of cover crops on insect pests and predators in conservation tillage cotton *J. Econ. Entomol.* **97** 1217–32

Tittonell P, El Mujtar V, Felix G, Kebede Y, Laborda L, Soto R L and De Vente J 2022 Regenerative agriculture—Agroecology without politics? *Front. Sustain. Food Syst.* **2** 6

Tscharntke T, Grass I, Wanger T C, Westphal C and Batáry P 2021 Beyond organic farming—harnessing biodiversity-friendly landscapes *Trends Ecol. Evol.* **36** 919–30

Ullmann K S, Meisner M H and Williams N M 2016 Impact of tillage on the crop pollinating, ground-nesting bee, Peponapis pruinosa in California *Agric. Ecosyst. Environ.* **232** 240–6

Virto I, Imaz M J, Fernández-Ugalde O, Gartzia-Bengoetxea N, Enrique A and Bescansa P 2015 Soil degradation and soil quality in Western Europe: current situation and future perspectives *Sustainability* **7** 313–65

Ward N 1993 The agricultural treadmill and the rural environment in the post-productivist era *Soc. Rural* **33** 348–64

Wauchope H S, Amano T, Geldmann J, Johnston A, Simmons B I, Sutherland W J and Jones J P 2021 Evaluating impact using time-series data *Trends Ecol. Evol.* **36** 196–205

Williams N M, Crone E E, Roulston T H, Minckley R L, Packer L and Potts S G 2010 Ecological and life-history traits predict bee species responses to environmental disturbances *Biol. Conserv.* **143** 2280–91

Wilson A and Gillings S 2002 Winter Farmland Bird Survey *BTO News* **241** 6–7

Woodcock B A, Isaac N J B, Bullock J M, Roy D B, Garthwaite D G, Crowe A and Pywell R F 2016b Impacts of neonicotinoid use on long-term population changes in wild bees in England *Nat. Commun.* **7** 12459

Woźniak A, Makarski B and Stepniowska A 2014 Effect of tillage system and previous crop on grain yield, grain quality and weed infestation of durum wheat

Yeo P and Corbet S A 2015 *Solitary wasps* Reprint Edn (Naturalists Handbooks) (Pelagic Publishing)

Zhao X, He C, Liu W-S, Liu W-X, Liu W-Y, Bai W, Li L-J, Lal R and Zhang H-L 2022 Responses of soil pH to no-till and the factors affecting it: a global meta-analysis *Glob. Change Biol.* **28** 154–66

Zielonka N B, Shutt J D, Butler S J and Dicks L V 2024 Management practices, and not surrounding habitats, drive bird and arthropod biodiversity within vineyards *Agric. Ecosyst. Environ.* **367** 108982