Predicaments in the collaborative management of farmland ecosystem services amid land circulation

The authors gratefully acknowledge the financial support from the Inner Mongolia Natural Science Foundation (No. 2026MS0825), the Inner Mongolia Social Science Foundation (No. 2026HQ03), and the National Natural Science Foundation of China (No. 32060317). We commit to providing the complete version of the data to relevant stakeholders. The authors are grateful to the experts who participated in the survey and the farmers for their cooperation and patience in the interview. The authors thank the anonymous reviewers for their help in improving this paper.

The financial support from the Inner Mongolia Natural Science Foundation (No. 2026MS0825), the Inner Mongolia Social Science Foundation (No. 2026HQ03), and the National Natural Science Foundation of China (No. 32060317).

1. Yajuan Chen, Sirui Bai are contributed to the work equally and should be regarded as co-first authors.

Authors and Affiliations

1. School of Economics and Management, Inner Mongolia Normal University, 81 ZhaowudaLu, Hohhot, 010022, China

   Yajuan Chen, Sirui Bai & Yaofeng Yang

2. Northwest Institute of Historical Environment and Socio-Economic Development, Shaanxi Normal University, Xi’an, 710119, China

   Yajuan Chen

3. School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, 710049, China

   Huyang Yu

Corresponding authors

Correspondence to
Yajuan Chen or Huyang Yu.

Competing interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethics approval

This study was conducted in accordance with the Declaration of Helsinki. The research protocol was approved by the School of Economics and Management, Inner Mongolia Normal University. Written informed consent was obtained from all individual participants involved in the questionnaire survey. Participants were fully informed of the study aims, data confidentiality measures, and their freedom to refuse participation or withdraw from the study at any time.

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Appendix A

Calculation of the CPSM index

The calculation method in this section draws references from the study by Zhao et al.⁶. The Crop Pollination Service Management (CPSM) Index is constructed based on four dimensions: dependency, contribution, sensitivity, and implementation. Dependency refers to farmers’ perception of the pollination reliance of their crops, as different crops exhibit varying degrees of dependence on pollinating insects. Contribution pertains to the frequency of flower visits by pollinating insects observed by farmers, which is influenced by landscape conditions and management strategies. Sensitivity indicates farmers’ perception of the extent to which crop yield or quality declines in the absence of pollinating insects. Implementation refers to the adoption by farmers of agricultural practices (pollinator-supporting practices) that promote the conservation of pollinating insects and enhance crop pollination services. The specific calculation steps are as follows:

(1) Select elements to be included in each dimension based on those related to the four principles of crop pollination service management. To ensure the relevance of these elements to Dengkou County, relevant literature was reviewed, discussions were held with multiple experts and scholars, and test interviews were conducted with the surveyed population. The specific elements of the evaluation index system for crop pollination service management are detailed in Table 14.

(2) Given the difficulty in quantifying the causal relationships among the four dimensions, and to address the complex interrelationships in crop pollination service management, this study employed the subjective weighting method and the Mini-Delphi technique to calculate the level of crop pollination service management by measuring the weighted sum of elements and dimensions. This approach effectively resolves the weight assignment issues for each dimension and element, accommodating the complexity and multidimensionality of crop pollination service management in real-world landscapes. During the weight determination process, the study consulted three experts in ecological economics with extensive knowledge of agricultural pollination, particularly pollination management, in Dengkou County. Initially, experts were individually asked to assign weights to each principle. Subsequently, based on the importance of selected elements under each principle as indicators of crop pollination service management in the local context, they assigned weights to each element. Experts were instructed to assign a weight of “1” to represent the least important principle and the least important element under each principle, and to rank all elements under a principle without an upper weight limit, where an element with a weight of “2” represents twice the importance of the least important element, and an element with a weight of “3” represents three times the importance. Additionally, experts were required to assign two weights to each element: one indicating the importance of maximizing the element, and the other indicating the importance of not enhancing the element. After the preliminary evaluations were completed independently by each expert, a meeting was organized to resolve potential misunderstandings regarding item descriptions or wording, discuss specific aspects, and provide justifications for their weight estimates. Following the meeting, a final round of individual assessments was conducted, and the average weights assigned by the three experts were calculated. These average weights were used in the subsequent construction of the index.

(3) First, calculate the maximum score for each dimension by summing up the maximum weights of all elements within that dimension, as shown in Eq. (15).

In Eq. (15), Max score of principle_i represents the maximum value of the i-th principle, and W_jimax denotes the maximum weight of the j-th element under the i-th principle. Subsequently, the scores for the four dimensions are calculated separately based on the weighted sum of the selected elements. Each dimension’s score is standardized by dividing the weighted sum of its elements by the maximum score of that dimension to eliminate the impact of variations in the number of elements within dimensions. The scores for dependency, contribution, sensitivity, and implementation are presented in Eqs. (16) through (19), respectively:

In Eq. (16), ({PS}_{I}) represents the dependency score of the respondent, and K_noj denotes the score of the j-th element within the dependency dimension. ({W}_{jImax/min}) refers to the maximum or minimum weight of the j-th element under the dependency dimension.

In Eq. (17), ({PS}_{II}) represents the contribution score of the respondent, Obs_j denotes the score of the j-th element within the contribution dimension, and ({W}_{jIImax/min})W_jⅡmax/min refers to the maximum or minimum weight of the j-th element under the contribution dimension.

In Eq. (18), ({PS}_{III}) represents the sensitivity score of the respondent, Per_j denotes the score of the j-th element within the sensitivity dimension, and ({W}_{jIIImax/min})refers to the maximum or minimum weight of the j-th element under the sensitivity dimension.

In Eq. (19), ({PS}_{IV}) represents the implementation score of the respondent, Pra_j denotes the score of the j-th element within the implementation dimension, and ({W}_{jIVmax/min}) refers to the maximum or minimum weight of the j-th element under the implementation dimension.

Thirdly, a weighted summation of the scores for each dimension is conducted to obtain the primary comprehensive index of crop pollination service management. The calculation formula is presented as Eq. (20):

In Eq. (20), (Primary_ICSM) represents the respondent’s score for the primary comprehensive index of crop pollination service management, and W_i denotes the weight assigned to the i-th principle of crop pollination service management.

Finally, the primary comprehensive index is linearly transformed into the final CPSM index with a value range between 0 and 100. The maximum comprehensive score is the weighted sum of the highest scores across all dimensions, which is 25.333, while the minimum comprehensive score is the weighted sum of the lowest scores across all dimensions, which is 12.999. The conversion formula is presented as Eq. (21):

(4) Element scoring and standardization. In terms of element scoring, the study employs a five-point Likert scale to convert responses to each element in the questionnaire into scores ranging from 1 to 5. Each element’s score is then divided by the maximum score of its corresponding principle to prevent overemphasis on certain elements within a dimension. Given the assumption that some elements are more important than others, differences in the maximum or minimum weights of elements are achieved by assigning weights to the minimum and maximum performance levels of elements. For example, respondents who perform a particular element at a Level 2 weight will receive 0.75 times the maximum weight and 0.25 times the minimum weight for that element.

Calculation of the IPM index

The calculation method in this section draws reference from the study by Yu et al.¹⁸. The computation of the IPM index involves five steps:

(1) Through a comprehensive review of relevant literature, pre-interviews with survey team members, and discussions with agricultural experts, IPM scholars, and practitioners, we determined the weights of each IPM principle and identified the most relevant practices under each principle within the context of the surveyed region, along with their respective weights. To prevent certain principles from being unduly amplified due to varying numbers of associated practices, the scores for each practice were standardized. In determining the weights of practices and principles, the research team consulted four seasoned agricultural experts (2 agricultural technology extension personnel from Dengkou County, 1 researcher specializing in IPM studies, and 1 university professor in the field of crop cultivation and environmental sustainability). The Mini-Delphi method (estimation-discussion-estimation) was employed for weight assignment. Initially, experts independently provided preliminary weights for each principle and practice, followed by discussions during an expert meeting. The final weights were determined by taking the average of the values after a new round of independent assessments (Table 15).

(2) Convert the questionnaire survey results into practice scores. The questionnaire primarily employed a five-point Likert scale, transforming responses to different questions into uniform scores of 0, 0.25, 0.5, 0.75, and 1, which served as the scores for each practice in the formula.

(3) Calculate the principle scores by weighted summation of the scores of each practice. The specific calculation method is shown in Formula (22):

In Formula (22), “({principle}_{i})” refers to the score of the i-th principle, “({practice}_{k})” denotes the score of the k-th practice, and “({Weight}_{kmax/kmin})” represents the maximum and minimum weights assigned to the practice by experts. To prevent the scores of certain principles from being unreasonably amplified due to varying numbers of practices under each principle, the score of each practice is divided by the maximum possible score for that principle, ensuring that the maximum value of Formula (22) is 1.0

(4) The scores for each principle are subjected to a weighted summation to compute the final IPM index, with the specific method outlined in Formula (23).

In Formula (23), ({Score principle}_{i}) similarly refers to the score of the i − th principle, and ({Weight}_{i}) denotes the weight assigned to the i-th principle by the experts.

(5) The IPM index scores are linearly transformed from their original range to a 0–100 score range.

References

Yu, H., Yang, Y., Chen, Y., Zhao, H., Xie, Y., & Zhang, Q. (2025). Examining the universality of the EU’s Integrated Pest Management (IPM) policy promotion in China: Asymmetric effects of farmer characteristics and cognitive factors on agricultural practices. Crop Protection, 192, 107167.

Zhao, H., Yang, Y., Chen, Y., Yu, H., Chen, Z., & Yang, Z. (2025). Configuration analysis of crop-pollination service management: a novel insight from the theory of planned behavior. PLoS One, 20(7), e0326226.

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