Honeybee pollination plays a significant role in sustaining the balance and biodiversity of sustainable rural development, agricultural production, and environments. However, little research has been carried out on the agricultural and economic benefits of pollination, especially for small farmers. This study investigated the adoption of honeybee pollination and its impact on farmers’ economic value using primary data from 186 kiwifruit farmers in three major producing districts, such as Pujiang, Cangxi, and Dujiangyan, in the Sichuan province of China. This study was conducted in two different steps: first, we used a bivariate probit model to estimate factors influencing honeybee pollination and artificial pollination adoption; second, we further used the Dynamic Research Assessment Management (DREAM) approach to analyze the influence of the adopted honeybee pollination economic impact. The results showed that: (1) growers with higher social capital, proxied by political affiliation, are more aware of quality-oriented products, and older growers tend to choose less labor-intensive pollination technology; (2) with the increase in labor costs, more kiwifruit growers would choose honeybee pollination, and more educated growers, measured by the number of training certificates, are more likely to adopt honeybee pollination; (3) the lack of awareness and access to commercial pollinating swarms hinders the adoption of honeybee pollination; (4) in addition to the economic benefit to producers, honey pollination also brings an even larger consumer surplus. This study suggests some policy recommendations for promoting bee pollination in China: raising farmers’ awareness and understanding of bee pollination through training, promoting supply and demand in the pollination market, and optimizing the external environment through product standardization and certification.

With the market-driven approach to agricultural technical services and the application of digital technology, digital agro-technical services have gradually emerged as a novel service model. However, there is a lack of empirical research on the effectiveness of this service in the academic literature. To address this research gap, this study measured the impact of this service on the technical efficiency and income levels of mango farmers, using data collected from 131 mango farmers in Hainan Province, China, from 2022 to 2024. This study employed the endogenous switching regression model (ESRM) and the inverse probability-weighted regression adjustment model (IPWRA) to analyze the data, addressing endogeneity through the instrumental variable method by replacing core explanatory variables and conducting sub-regional regression for robustness testing. The main research conclusions are as follows: Under the counterfactual assumption of ESRM, farmers who adopt this service would have experienced a decrease in technical efficiency by 0.025 (a decline of 3.6%) if they had not adopted the service. Conversely, farmers who did not adopt it would have seen an increase in technical efficiency by 0.047 (an increase of 7.3%) if they had chosen to do so. Additionally, under the post-treatment income effect estimation using IPWRA, compared to farmers who did not receive the service, those who did so saw an income increase of 15.6%. The analysis results from methods such as K-nearest neighbors matching also confirm this conclusion. Therefore, it is evident that digital agro-technology services play a significant role in improving the technical efficiency and income levels of small-scale farmers.

Sustainability in agricultural development relies on continuing seed (cultivar) development and adoption. Measuring the contribution made by new seed cultivars to yield increases nationwide is essential, but this process remains challenging for academics. This study develops a method to estimate the overall contribution of new seed cultivars to crop yield increases by dividing the study period into several diffusion periods in terms of the replacement in major seed cultivars, and by defining a yield response model incorporating a series of dummy variables to capture net increases due to new cultivars’ diffusion in each period. According to this method, corn cultivars tested in the period from 1980 to 2013 in China can be divided into five periods. Significant increases in corn yields due to cultivar replacement were captured, and the contributions over the base period were calculated as 61.7%, 47.6%, 46.0%, and 48.9% for periods 2, 3, 4, and 5, respectively. Their respective contributions over the previous periods were therefore 61.7%, 34.6%, 26.7% and 56.7%. Estimation results indicate that the introduction of new corn seed cultivars significantly contributed to corn yield increase. This method is likely to have practical applications for other crops or livestock.

Apiculture is an important industry closely related to the national economy and people’s livelihoods. Beekeepers’ behavior is an important factor affecting the yield, quality, and benefits of apiculture. However, there is a lack of a systematic understanding of the long-term changes in beekeeping decisions made by beekeepers. Using panel data, we analyzed the dynamic trends and related influencing factors of decisions made by beekeeping models, honey source plant selection, and the migration flow space of beekeepers from 2009 to 2020. The results showed that the proportion of the LMB model decreased, while the PAB and SMB models continued to increase, the frequency of utilization of the main nectar source plants for honey collection decreased, and the concentration of migratory flow of beekeeping increased. Behavior of beekeepers from 2009 to 2020 showed a certain degree of spatial contraction, which seriously restricted the effective use of nectar plant resources. Family attributes, economic status, beekeeping models, and disaster conditions directly or indirectly affected beekeepers’ decisions. We propose a series of recommendations to facilitate the transformation and advancement of the Chinese bee industry. This study promotes an understanding of sustainable development of the bee industry in China and other countries worldwide.

The eastern honeybee (Apis cerana) is a keystone pollinator for native ecosystems and agricultural crops in China. However, its distribution faces significant uncertainty due to accelerating climate change. To quantify these risks and inform management strategies, we employed an ensemble species distribution model (Biomod2) integrating ten algorithms to project the suitable habitat of the eastern honeybee under current and future (2060s and 2100s) climate scenarios (SSP126, SSP245, and SSP585). The ensemble model achieved an excellent predictive performance (AUC > 0.9, TSS > 0.8). The current suitable habitat spans approximately 1.47 million km², primarily south of the Yangtze River. Biomod2 simulation indicates that the precipitation of the wettest month and mean diurnal temperature range are the dominant environmental stressors influencing the shift in the spatial distributions of the eastern honeybee. Comparisons between current and future climate scenarios reveal a distinct trend of spatial range contraction in southern China and a northwestward shift of the habitat centroid. The most severe impact is projected under the SSP585 scenario, with a potential net habitat loss of 42.25% by 2100. We propose a dynamic conservation strategy that prioritizes the protection of southern climate refugia while managing habitat connectivity to facilitate the species’ migration, thereby safeguarding agricultural resilience.

Managed honeybee colonies contribute to agriculture through multiple ecosystem services. However, the contribution of honeybees is often assessed through bee products rather than through the crop pollination services they provide. This study quantified the economic value, relative vulnerability and supply–demand imbalance of honeybee pollination in Chinese agriculture from 2010 to 2024. Using crop production, gross production value, harvested area and managed colony data, combined with crop-specific pollination-dependence coefficients, we estimated pollination function quantity, service value, relative vulnerability, theoretical colony demand and supply gaps. Benchmark pollination service value increased from US$114.27 billion in 2010 to US$247.02 billion in 2024, while pollination-attributable output increased from 203.92 million tonnes to 274.73 million tonnes. In 2024, 36.94% of selected crop value was exposed to pollination dependence, with vegetables and fruits contributing more than 93% of total benchmark value. Effective honeybee supply was estimated at 5.73 million colonies in 2024, resulting in a substantial supply deficit of 68.00 million colonies and a demand-to-effective-supply ratio of 12.88 under the benchmark scenario. Despite sensitivity to colony-use assumptions, the demand–supply gap remained persistent across alternative specifications. These results show that China’s high-value agriculture increasingly depends on pollination services while managed pollination capacity remains far below theoretical demand.