Data covering soil management practices and farm characteristics on Swiss arable farms

Sampling procedure

In the context of the Horizon Europe Project “InBestSoil”, the data collection focused on arable management practices in Switzerland. Specifically, those practices related to soil health and soil conservation undertaken within the 2022/2023 production season. Farm selection for the survey was based on specific criteria to ensure that the data collection accurately represented arable agricultural practices in Switzerland. These criteria were designed to target farms that were significantly involved in arable agriculture, which is crucial for assessing arable soil health management practices. Eligible farms were required to meet the following criteria:

• Grow wheat in the preceding season (2021/2022).

• Farm at least 3 hectares of arable land in the preceding season (2021/2022).

• Arable land must have comprised at least 20% of the total farmed area in the preceding season (2021/2022).

We entered a data sharing agreement with the Federal Office of Agriculture to enable our survey campaign via access to contact information of all farmers who met the above selection criterion (see the supplementary material in the data repository for a copy of this contract)¹. The Federal Office of Agriculture implemented our selection criterion on the agricultural data that they collect on a yearly basis from the direct payment applications of all Swiss farmers. Note, at the time of our application to the Federal Office of Agriculture, data for the production season 2022/2023 was not available. This is why we use data from the preceding production season for specifying the selection criteria, as this was the latest data available at the time, from which the Federal Office of Agriculture could make an assessment of which farm contact details to share with us for the survey.

In August 2023, we received the contact details of 15,023 farmers who qualified for the survey from the Federal Office of Agriculture’s records. The information we received included the email address, farm identification number, language spoken, name and form of address. However, as per our data sharing agreement with the Federal Office of Agriculture, this data was allowed exclusively for our use in this project and cannot be shared with any outside partner not party to the aforementioned data sharing contract. The contact data of farmers that was received from the Federal Office of Agriculture will be kept for the duration of the InBestSoil project and stored securely on private institutional servers in encrypted files. All contact information will be deleted at the conclusion of the project (December 2026) and all data presented herewith is strictly anonymised to protect the data and identities of the farmers who took part in the survey. Moreover, we have taken measures to prevent any farmers from being identified via their answers (for example variables such as manager age, wheat areas grown, location etc. have been classified into more homogenous categorical groups), which means that the data we present here is slightly different to the data that we have available for our own analyses, as agreed under the data sharing agreement with the Federal Office of Agriculture.

Survey design and content

While adoption of agricultural practices certainly varies with farm characteristics such as size, labour availability, or participation in agri-environmental schemes, these factors alone are not sufficient to explain farmer behaviour. There is no single set of drivers that consistently predicts adoption across studies or regions⁴³. Instead, adoption depends strongly on local contexts, and the interplay of economic, social, and psychological factors⁴⁴. To capture the complexity of adoption behaviour, the survey included questions on farmers’ priorities, perceptions, self-assessed competencies, and personal goals, as well as their exposure to peer practices, participation in training and advisory services, and sources of information. These dimensions are important because farmers do not make decisions in isolation; their attitudes towards risk, innovation and environmental values can influence their decisions alongside financial considerations. Such data contribute to a more thorough understanding of the multifaceted factors influencing soil health-related decisions. The inclusion of these variables also offer valuable insights into the barriers and drivers of sustainable soil management, essential for shaping targeted and effective agricultural policies and support programs.

The full survey is available within the data repository in French, German and English¹. The final survey was developed over the course of a year, including revisions resulting from three rounds of consultation with external stakeholders, internal consultation and testing with farmers. All participants in the survey were asked to give their informed consent by ticking a box in the online questionnaire, confirming their agreement to participate in the study. Additionally, participants consented to the linking of secondary geographical data with their responses, which was also confirmed by ticking a separate checkbox in the survey. Once the participants had agreed to these, the survey was administered uniformly following the structure outlined below. All questions appeared in the same order and, only if certain exclusion criteria were met – such as when their previous answer ruled out any further sub-questions – were some sub-questions hidden from the view of participants. Inclusive of all sub-questions, the survey contained 57 questions, and answering the questionnaire took farmers a median time of 23 minutes.

The survey design was based on previously implemented surveys regarding agricultural production practices in Switzerland^{45,46,47,48,49}. Specifically, questions on farm information and participation in soil-related programmes were included to assess farmers’ engagement with policy incentives and voluntary schemes. The inclusion of personal characteristics aimed to understand demographic drivers of management behaviour. The questions on management practices were developed in close collaboration with experts from the soil science and agricultural extension fields, and were cross-checked with relevant literature. Data on milling wheat production and related input use were collected to link agronomic decisions with productivity outcomes. Information on structural farm characteristics, such as farm type, location, and land tenure, provides context for understanding the decision-making environment and potential constraints faced by farmers. Finally, a strong focus was placed on behavioural and attitudinal factors, including information sourcing, perceived risks, and personal goals, to account for the cognitive and motivational dimensions of farmer behaviour. The following section provides an overview of the variables investigated within each of these question groups. The collected data are documented in the accompanying datasets¹. Each question group corresponds to a clearly defined set of columns.

Demographic details (Primary dataset columns B-H)

Age, duration farm responsibility, gender, full time equivalent and whether the farm succession is already secured.

Participation in soil health programmes (Primary dataset columns H-S)

Organic farming support, soil cover scheme, reduced tillage scheme, herbicide-free farming scheme, pesticide-free farming scheme, efficient fertiliser use, wider row planting, beneficial insect strip, precision application, cantonal soil health support, cantonal input reduction support, cantonal investment and equipment support.

Management Practices (Primary dataset columns T-CA)

An overview of all management practices addressed in the survey, including their descriptions and the typical machinery used, is provided in Table 1. Farmers were asked about their knowledge about the practices, the application as well as the frequency of application within the last 10 years and whether they know other farmers that use the practice. The practices covered by our survey were selected based on the input of soil scientists and agricultural extension workers based in Switzerland.

Milling Wheat Production (Wheat dataset columns B-M)

Production standard, hectares of milling wheat grown, yield milling wheat, yield milling wheat over last five seasons, quantity synthetic fertiliser, quantity organic fertiliser, sowing density, number of biostimulant treatments, number of herbicide treatments, number of fungicide treatments, number of insecticide treatments and number of plant growth regulator treatments.

Structural Farm Characteristics (Primary dataset columns CD-CP)

Family members employed, farm focus (arable, livestock, permanent crop, others), full time or part-time farm, percentage of rented land, whether the soil has been assessed and a soil management plan exists.

Training and Advice (Primary dataset columns CQ-CZ)

Advice agricultural adviser, advice agricultural retailer, advice cantonal or national institution, consult other farmers, consult social media channels, consult publications or webpages, participation equipment demonstration, participation farmer discussion or training group, participation farm demonstration, participation course.

Behavioural and Attitudinal Factors (Primary dataset columns DA-EK)

Respondents’ self-assessment of their perceived influence of the weather on crop production and ambitiousness of self-set production goals.

Respondents’ self-assessment of their willingness to take risks in the domains of; agricultural production, investment in agricultural technology and crop protection.

Respondents’ self-assessment of their confidence in being able to; find solutions to arable production challenges and achieve production goals by harvest end.

The respondents self-reported importance of the following aspects in decision making;

Maximising yields, minimising input costs, minimising time or labour requirements, minimising production risks, minimising farm exposure to weeds or pests or diseases, adapting to weather patterns, adapting to farmland conditions, improving soil health or structure or fertility, improving biodiversity, minimising environmental impact, expanding farm land, adapting to crop market developments, adapting to changes in direct payment rates or regulations, seeking professional agronomic, seeking casual advice from friends or colleagues and seeking peer approval.

Ethical approval and pre-registration

The survey campaign and research design were both approved separately by the ETH Zürich Ethics Commission as proposal 2023-N-212 as well as the FiBL Ethics Committee as proposal FSS-2023-006. Copies of the approval letters are included in the supplementary material¹. Before launching our survey, we also submitted two research plans for pre-registration of hypotheses via the online platform AsPredicted operated by the University of Pennsylvania (link: AsPredicted). For further information on these, see AsPredicted #153145 and AsPredicted #153146 that were registered on 29^th November 2023.

Survey implementation

The survey was implemented as an online survey formulated with Lime Survey and distributed via email. All eligible farms received an individualised email addressed personally to the recipient and a survey link, connected with a unique token to enable us to link the farmer responses with secondary data available for each farm. The participants were asked to give their permission for this by approving the terms and conditions we made available to them regarding how their data would be handled. By agreeing to the disclosure agreement, the farmers gave their permission for the anonymised data, that they subsequently provide through the survey, to be used exclusively for science and research purposes. Farmers were also given the option to opt out of the survey at any time, with no explanation needed. To incentivise participation in the survey we offered the opportunity to enrol in a lottery of 100 supermarket vouchers worth CHF 150 each and the option to receive a personalised results report comparing the farmers’ answers to the answers of other similar farms. The individualised reports were administered via a bespoke app created using R-Shiny (see technical validation section below for further details).

Prior to the full survey launch, a pilot survey was conducted on a random sample of 1% of eligible farms (150 farms) to test the survey’s functionality and to refine any issues. The pilot survey launched on 30^th November 2023, and the full survey went live six days later, on 6^th December 2023. The survey was closed on 31^st January 2024, after a response period of nearly two months.

Data cleaning

To minimise errors already at the point of data entry, the survey was designed to allow only predefined values or plausible numeric ranges for most variables. Wherever this was not technically feasible, such as in open-text fields or free numeric input, we conducted systematic data cleaning after data collection. Data cleaning involved addressing inconsistencies and missing values. In cases where values were deemed implausible or outliers, they were either removed or corrected if sufficient data from other columns was available. This cleaning procedure was applied to variables related to plant protection product treatments, yield, sowing density, labour input, and demographic information. We include the following to illustrate the approach we took as an example (note all processing codes are available in the supplementary material which outline these decisions on a line-by-line basis):

If in the labour units column, an entry was listed as 48, which was inconsistent with the farm area, this value was corrected to 4.8 using a related column for recalculation. Similarly, we proceeded for the variable age: if a data entry was obviously wrong, such as a year of birth recorded as 60 instead of the demanded format YYYY (1960), and the farmer had entered the column of farming experience 40 years, the value was corrected to ‘1960’ based on logical inference. If no reliable correction could be made, the value was marked as ‘NA’ (Not Available).

To ensure anonymity, apart from removing precise geographical information we also grouped continuous variables such as age and farming experience into categories (e.g. age_group and years_experience_group). The data was anonymised, and no specific details were included that could link individual responses to specific farms. No randomisation was applied to the data. With regard to the secondary data, we also took measures to prevent identification by rounding the variables to the nearest integer (the codes for the processing of this data are also available in the supplementary material).