A Field Workflow to Assess Environmental Communication in Agricultural Heritage Landscapes

Summary

Here, we present a protocol to link visitor route records, ecological disturbance indicators, QR-code backend logs, and on-site questionnaires to identify location-specific environmental communication gaps in agricultural heritage landscapes.

Abstract

Agricultural heritage landscapes are living socio-ecological systems where tourism intersects with agricultural production and biodiversity conservation. Evaluating environmental communication in these settings requires spatially explicit evidence rather than relying only on post-visit satisfaction surveys. This protocol integrates four data streams: station-passage visitor route records, repeated station-level ecological disturbance observations, anonymized QR-code interpretation backend logs, and on-site visitor questionnaires. The workflow is designed for compact, walkable agricultural heritage sites where stable observation stations can be established, and daily digital-log exports are available. Researchers first zone the site and establish fixed sampling stations, then deploy station-specific QR-code interpretation pages, record visitor station sequences without continuous GPS tracking, monitor ecological disturbance indicators, and link anonymized route and questionnaire records for analysis. In a 30-day representative application, the workflow identified three visitor route typologies, quantified station-level disturbance, screened QR-code data quality, and located relative communication gaps where ecological pressure was high compared with digital engagement. QR-code exposure was positively associated with heritage understanding and perceived ecological sensitivity, whereas satisfaction showed no statistically robust association. This method provides a reproducible field protocol for managers who need to align digital interpretation, route management, and ecological monitoring in living agricultural heritage landscapes.

Introduction

Agricultural heritage landscapes function as active socio-ecological systems characterized by the coexistence of agrobiodiversity, traditional knowledge, landscape practices, and local livelihoods¹. Because tourism in these settings occurs within working production spaces, such as irrigation networks, village paths, and field margins, its development must remain aligned with the ecological and cultural functions of the site to support dynamic conservation^2,3. Evaluating the sustainability of such tourism requires studying the continuous interactions among land use, biodiversity, and visitor behavior⁴. Consequently, environmental communication in these landscapes should be assessed not only through general visitor satisfaction, but by examining whether interpretive messages successfully reach the specific locations where ecological sensitivity and visitor pressure overlap.

Effective heritage interpretation must be purposeful and site-specific, revealing localized meanings rather than merely transmitting factual information^5,6. In agricultural tourism, this implies that visitors need a clear context to understand why a field margin requires protection or why a rice-fish plot holds conservation value⁷. Digital interpretation tools, particularly QR codes, offer a practical mechanism to deliver location-linked messages without cluttering working landscapes with intrusive physical signage^8,9. More importantly, the backend logs generated by QR-code scans provide traceable behavioral data on interpretation exposure, allowing researchers to evaluate engagement objectively.

Conventional assessments of visitor interpretation have typically relied on post-visit questionnaires. While useful for capturing cognitive outcomes like heritage understanding or perceived ecological sensitivity¹⁰, single-instrument surveys often introduce common-method bias when simultaneously measuring exposure, perception, and behavioral intention¹¹. Furthermore, traditional methodologies struggle to capture the spatial dimension of visitor impacts. Although continuous GPS tracking can map visitor distribution and off-route movement¹², it often raises privacy and feasibility concerns in inhabited heritage villages. Similarly, while recreation ecology emphasizes that visitor impacts are shaped by site resistance, activity type, and spatial concentration rather than mere visitor volume¹³, these physical indicators are rarely integrated with communication assessment data¹⁴.

To bridge these methodological gaps, this article presents an integrated field workflow designed to map and assess environmental communication. The workflow is most suitable for compact, walkable agricultural heritage landscapes in which 8–12 stable observation stations can be maintained, visitor flow is sufficient for systematic exit sampling, and a QR-code backend can export daily station-level logs. The primary goal of this protocol is to establish a verifiable spatial linkage between visitor movement, site-level ecological conditions, digital interpretation usage, and cognitive outcomes. By combining station-passage route records, standardized ecological disturbance observations (e.g., trampling, litter, and bare soil), QR-code backend logs, and targeted on-site surveys, the method enables researchers to evaluate environmental communication as a continuous, localized field process. Ultimately, this workflow provides site managers with a practical diagnostic framework to identify specific communication gaps-landscape nodes where relative ecological disturbance is high but interpretive engagement remains insufficient-thereby guiding more precise monitoring, intervention planning, and post-intervention evaluation in agricultural heritage tourism.

Protocol

All procedures involving human participants were reviewed and approved by the Institutional Review Board of Communication University of China (IRB Approval Number: CUC2026A002; approval date: January 5, 2026). The study was conducted in accordance with the Declaration of Helsinki, the Measures for the Ethical Review of Life Sciences and Medical Research Involving Humans, and relevant institutional guidelines. Written or electronic informed consent was obtained from each participant before visitor-route recording and questionnaire administration.

1. Site zoning and sampling station setup

1. Execute the field-based assessment workflow to evaluate environmental communication and capture both visitor communication outcomes and site-level environmental conditions¹⁵. Refer to Figure 1 for the complete visual representation of the field procedure.
2. Select a compact, walkable agricultural heritage site comprising active agricultural production spaces, such as terraced fields, rice-fish plots, irrigation channels, and village lanes.
3. Before fieldwork, record the site name, administrative region, approximate route scale, seasonal context, and central World Geodetic System 1984 (WGS84) coordinate in the station log. For the representative application, use a rice-fish terraced heritage landscape with a designated visitor route of approximately 0.5–3.9 km and ten stable observation nodes.
4. Establish 10 fixed sampling stations across the landscape and code them as S01 through S10. Define these stations to ensure that the protocol remains general and applicable to other agricultural heritage landscapes.
5. Select stations that represent major visitor-use areas and ecologically or culturally important features of the study site.
6. Ensure that the selected stations represent major visitor-use areas and ecologically or culturally significant features, such as entrance and exit points, primary landscape viewing areas, agricultural production zones, and sensitive path segments¹⁶.
   NOTE: In this representative application, ten stations were established to cover the visitor entrance (S01), terraced-field viewing points (S02 and S03), an irrigation-channel node (S04), a rice-fish co-cultivation plot (S05), a traditional farming-tool display area (S06), a village lane (S07), a pond-edge viewing platform (S08), a sensitive field-margin path (S09), and the exit orientation point (S10).
7. Retain a station for the final protocol only if it meets three specific criteria. Verify that it is accessible to visitors without entering private farmland, possesses a stable boundary for repeated observation, and allows observation without interrupting local farming activity.
8. Georeference each station using a GPS-enabled mobile device with approximately ≤ 5 m horizontal accuracy. Record the coordinates in WGS84 format, verify them on two separate pre-field days, and retain a station only when the repeated coordinates and visible boundary markers remain within 5 m of the predefined observation boundary. Refer to Table 1 for the detailed sampling stations and field observation arrangement.

2. QR-code environmental interpretation deployment and tracking

1. Install QR-code interpretation points at all 10 sampling stations prior to formal fieldwork. Use a QR-code backend system capable of exporting daily CSV or XLSX files with station ID, timestamp, anonymized or hashed device ID, total scans, unique users, dwell time, scroll depth, repeat scans, and technical-error fields.
2. Link each QR code to a dedicated mobile page containing one short text, one site photograph, one environmental or heritage message, one behavioral reminder, and an optional audio explanation.
3. Limit the text on each interpretation page to 180–260 Chinese characters and focus on a single theme per station. Design the S04 page to explain irrigation-channel heritage and water conservation, while using the S09 page to explain field-margin protection and visitor-route discipline. Use neutral phrasing for all behavioral reminders.
4. Configure the backend access logs to record total scans, unique users, median dwell time, content completion rate, repeat scans, technical errors, and investigator test-device exclusions. Export the log in CSV or XLSX format at the same time each day and retain an unedited raw copy before screening.
5. Apply strict validity rules to the QR-code tracking data. Consider a scan valid only when the interpretation page remains open for at least 5 s. Define a completed interpretation exposure as a dwell time of at least 30 s or a scroll depth reaching at least 80% of the page.
6. Treat multiple scans from the same hashed device at the same station within a 10-min window as a single unique station-level exposure and exclude investigator test-device scans before analysis.

3. Investigator training and field quality control

1. Conduct a two-day training session for all investigators prior to formal fieldwork. Dedicate the first day to covering station boundaries, route coding, ecological indicators, QR-code log definitions, questionnaire administration, and data-entry rules.
2. Perform a field rehearsal on the second day using 20 pilot visitors. Exclude these pilot data from the final analytical dataset.
3. Check inter-observer consistency during the pilot phase. Ensure that the absolute difference in visually estimating bare-soil proportion between two investigators remains within 10 percentage points. Maintain a difference of no more than one level for the trampling score¹⁷ and no more than two persons or two litter items for paired visitor-count and litter-count records.
4. Proceed with the formal field protocol only when at least 85% of the paired pilot observations meet the predefined consistency criteria. Initiate immediate discussion and re-training if visitor count and litter count discrepancies exceed two persons or two items, respectively.
5. Evaluate the digital questionnaire during the pilot phase. Retain the final version of the survey only after confirming the average completion time falls between 6 min and 10 min.

4. Fieldwork scheduling and visitor route recording

1. Conduct the daily fieldwork from 09:00 to 17:30 over a 30-day continuous period. Divide each day into four observation blocks comprising 09:00–10:30, 10:45–12:15, 13:30–15:00, and 15:15–16:45.
2. Export the QR-code log data from the backend system at 18:00 each day.
3. Record visitor routes continuously during the fieldwork period using a station-passage method to assess spatial behavior without continuous personal GPS tracking¹⁸. At entry or recruitment, assign each consenting participant a non-identifying alphanumeric route token. Record this token at each station passage and again at questionnaire completion; do not collect names, telephone numbers, or full personal GPS trajectories.
4. Record the anonymous route token, entry station, station sequence, start time, end time, number of stations visited, QR-code stops, route deviation, and exit location for each consenting participant. Store the temporary route-token linkage file separately from the analytical dataset and remove direct identifiers before analysis.
5. Validate route record based on four explicit conditions. Verify that the visitor enters S01 or joins the formal route before S03, records at least three distinct stations, spends a total route duration between 20 and 240 min, and completes exit confirmation at S10 or an authorized temporary exit.
6. Estimate route length using pre-measured distances between adjacent station segments. Measure each segment independently with a GPS-enabled mapping application and a measuring wheel or laser distance meter where terrain permits. Repeat the measurement when the two estimates differ by more than 5% and use the average verified segment length for route-distance calculation.
7. Code route deviation as 0 when the visitor remains on designated paths and 1 when the visitor enters sensitive non-designated spaces such as field margins or pond edges. Calculate sensitive zone overlaps as the percentage of the recorded route located within 10 m of active ecological fields.

5. Ecological disturbance monitoring and index calculation

1. Measure ecological disturbance at the station level utilizing repeated short-interval observations to link visitor pressure directly with observable environmental indicators¹⁹. Execute these ecological observations specifically during the middle 15 min of each daily observation block.
2. Define the formal observation area at each station as a 10 m × 10 m plot. Mark an equivalent 100 m² observation area to maintain consistency if the station topography does not allow for a square plot.
3. Record prevailing weather conditions for each block. Exclude the ecological observation from the disturbance-index calculation if continuous rainfall lasts longer than 30 min. Retain the data and code for light rain lasting less than 30 min as a field-condition variable.
4. Count the precise number of visitors entering the observation area during the 15-min window. Count visible visitor-generated waste items, including plastic and paper, to determine the litter count. Estimate the bare-soil proportion visually in 5% increments.
5. Score visible trampling on a discrete scale from 0 to 4. Assign 0 for no visible trampling, 1 for slight vegetation flattening, 2 for repeated footmarks, 3 for continuous soil exposure along informal walking lines, and 4 for severe trampling with vegetation loss or visible soil compaction.
6. Measure water turbidity exclusively at the water-related stations (S04, S05, and S08) using a portable turbidity meter covering at least 0–25 NTU with 0.1 NTU resolution; verify the meter each field day with 0 NTU and 20 NTU calibration standards or manufacturer-equivalent standards.
7. Use an A-weighted sound-level meter meeting Class 2 or equivalent performance, calibrate it at 94 dB and 1 kHz before daily use, place it 1.5 m above the ground and at least 2 m away from the investigator, and measure ambient noise for 60 s. Record the number of visible or audible birds detected within a 50 m radius.
8. Calculate a composite ecological disturbance index for each station-observation record²⁰. Standardize each raw indicator to a 0–1 scale using fixed reference bounds rather than site-specific min-max values: visitor count, 0–35 visitors per 15 min; litter count, 0–8 items; bare-soil proportion, 0%–40%; trampling score, 0–4; noise, 45–70 dBA; turbidity, 0–25 NTU; and bird count, 15–0 birds within 50 m after reverse coding. Truncate standardized values below 0 or above 1 to the 0–1 range before weighing.
9. Apply a fixed weighting scheme consisting of 20% for visitor count, 20% for trampling score, 15% for bare-soil proportion, 15% for litter count, 10% for noise level, 10% for water turbidity, and 10% for the reverse-coded bird count. For non-water stations, code turbidity as not applicable and redistribute its 10% weight equally to trampling score and bare-soil proportion.
10. Report a sensitivity check using a common six-indicator index that excludes turbidity to confirm that the station ranking is not driven by heterogeneous weighting.
11. Classify the final index, which ranges mathematically from 0 to 100, into discrete disturbance levels. Define low disturbance as less than 35, moderate disturbance as 35 to 59.9, and high disturbance as 60 or above.
12. Treat weekly station means of 60 or higher, station-day mean exceedances of 60, and any single observation above 75 as separate monitoring prompts; do not use the relative communication-gap matrix as a substitute for these absolute management thresholds. Consult Table 2 and Supplementary Table S2 for operational variables, coding rules, fixed standardization bounds, and threshold values.

6. Participant recruitment and on-site questionnaire administration

1. Administer a structured on-site questionnaire using tablet devices and a survey platform capable of exporting CSV or XLSX files. Measure visitor background, route experience, QR-code exposure, perceived interpretation quality, heritage understanding, perceived ecological sensitivity, place attachment, pro-environmental behavioral intention, satisfaction, and willingness to pay²¹. Provide the questionnaire domains and coding rules in Table 2.
2. Measure all attitudinal and cognitive items on a five-point Likert scale ranging from strongly disagree to strongly agree. Measure stated willingness to pay utilizing six ordered numerical categories.
3. Recruit participants via systematic intercept sampling at the main exit (S10) strictly after visitors complete the main interpretive route. Conduct secondary recruitment at S07 only when the visitor flow at S10 proves insufficient.
4. Invite every third eligible adult visitor to participate in the study. Approach the next eligible visitor immediately and restart the sampling interval if the selected visitor refuses.
5. Screen all potential participants based on four inclusion criteria. Ensure the participant is at least 18 years old, has completed a minimum of three stations, presents a route duration of at least 20 min, and demonstrates the cognitive ability to complete the questionnaire independently. Explicitly exclude local staff, vendors, tour guides, researchers, and site management personnel from the sample.
6. Continue field data collection until reaching the planned valid sample size of 240 questionnaires to effectively balance analytical needs with visitor-flow feasibility²². Restrict collection to a maximum of 12 valid questionnaires per weekday and 16 valid questionnaires per weekend or public holiday day to prevent temporal sampling bias.

Results

The successful implementation of this protocol is evidenced by the seamless integration of multi-source data streams. Specifically, the coherence between visitor movement patterns, real-time ecological disturbance observations, and QR-code engagement logs demonstrates that the workflow effectively captures location-specific environmental communication gaps in complex agricultural heritage landscapes.

Visitor route characteristics and spatial distribution

During the ...

Discussion

The presented protocol establishes an integrated field workflow for assessing environmental communication in agricultural heritage landscapes. By combining visitor route records, ecological disturbance indicators, QR-code backend logs, and on-site surveys, the method bridges the analytical gap between digital communication exposure, localized environmental conditions, and visitor cognitive outcomes. Representative results show that QR-code interpretation exposure was associated primarily with cognitive and perceptual out...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Data visualization software	Tableau	Desktop 2024	Optional software for figure preparation; any equivalent visualization software may be used.
Digital survey software	Qualtrics	Research Core	Tablet-based questionnaire platform capable of exporting CSV/XLSX files.
Handheld GPS receiver	Garmin	GPSMAP 66i	GPS-enabled device with approximately less than or equal to 5 m horizontal accuracy.
Portable sound-level meter	Equivalent Class 2 sound-level meter manufacturer	Model/catalog number to be confirmed by authors	A-weighted meter meeting Class 2 or equivalent performance.
Portable turbidity meter	Equivalent portable turbidity meter manufacturer	Model/catalog number to be confirmed by authors	Portable meter with calibration standards covering the observed NTU range.
QR-code interpretation system	CLIAO	Enterprise Edition	QR-code backend capable of exporting station ID, timestamp, hashed device ID, dwell time, scroll depth, repeat scans, and technical errors.
Statistical analysis software	R Foundation	v4.3.0 or later	Statistical computing environment used for analysis.
Tablet computer	Any tablet manufacturer	Model/catalog number to be confirmed by authors	Tablet device used for on-site questionnaire administration.