Search Results (20)

Rural tourism plays a crucial role in promoting industrial revitalization in mountainous regions. Drawing inspiration from the site selection mechanisms of nature reserves, this study constructs a gap analysis framework tailored to rural tourism destinations, aiming to provide technical support for their spatial layout and systematic planning. By integrating a potential evaluation system based on tourism resources, market demand, and synergistic factors, the study identifies rural tourism priority zones and proposes a development typology and spatial optimization strategy across five provinces in Southwest China. The findings reveal: (1) First- and second-priority zones are primarily located in the core and periphery of provincial capitals and prefecture-level cities, while third-priority zones are concentrated in resource-rich areas of Yunnan and Guizhou and market-oriented areas of Sichuan, Chongqing, and Guangxi. (2) The Chengdu Plain emerges as the core region for rural tourism development, with hotspots clustered around Chengdu, northern and western Guizhou, central Chongqing, eastern Guangxi, and northwestern Yunnan, whereas cold spots are mainly situated in the western Sichuan Plateau and the Leshan–Liangshan–Zhaotong–Panzhihua–Chuxiong–Pu’er belt. (3) The alignment between tourism resources and rural tourism destinations is highest in Yunnan and Guizhou, while Chongqing exhibits the strongest match between destinations and tourism market potential and synergistic development conditions. Overall, 79.35% of rural tourism destinations in the region are situated within identified priority zones, with Chongqing, Guizhou, and Sichuan exhibiting the highest proportions. Based on the spatial mismatch between potential and existing destinations, the study delineates four development types—maintenance and enhancement, supplementation and upgrading, expansion, and reserve development—and offers regionally tailored planning recommendations. The proposed framework provides a replicable approach for spatial planning of rural tourism destinations in complex mountainous settings. Full article

Accurate estimation of forest canopy height and understory terrain in mountainous regions is crucial for carbon stock assessment under the Paris Agreement but remains challenging. This study aimed to evaluate ICESat-2’s performance in these complex environments. To achieve this, ICESat-2 ATL03 Version 6 photon data were processed using a novel adaptive DBSCAN algorithm (BDT-ADBSCAN) in Pu’er City, China, a biodiversity hotspot, and results were validated against airborne LiDAR. ICESat-2 achieved high terrain retrieval accuracy (R² = 1.00, RMSE = 0.91 m), primarily affected by slope, while canopy height retrieval was less accurate (R² = 0.53, RMSE = 6.45 m) with systematic underestimation, mainly influenced by canopy height itself. Nighttime strong-beam acquisitions substantially improved accuracies for both products. This research demonstrates ICESat-2’s viability for high-resolution digital terrain modeling and provides quality control thresholds for forest structure estimation in challenging regions, addressing validation gaps in Asian biodiversity hotspots and supporting carbon monitoring for UN Sustainable Development Goals. Full article

In countries and regions where Asian elephants are distributed, human–elephant conflict has become an important ecological and socio-economic issue. As one of the major habitats of Asian elephants, China faces severe challenges. Based on the theory of planned behavior and the risk perception theory, this study takes the survey data of 449 smallholder farmers in the Asian elephant distribution areas of Pu’er City, Yunnan Province as samples and uses the Tobit model and the mediating effect model to empirically analyze the impact of human–elephant conflict on farmers’ land use efficiency and its mechanism. The results show the following: (1) The human–elephant conflict risk perception has a significant negative impact on farmers’ land use efficiency. A one-unit increase in risk perception decreases land use efficiency by 250.34 CNY/mu. (2) Social networks positively moderate the negative impact of the human–elephant conflict risk perception on farmers’ land use efficiency, further strengthening the negative impact of risk perception. (3) From the perspective of the mechanism, the human–elephant conflict risk perception increases the likelihood of farmers changing their land use behavior. Farmers with high risk perception tend to reduce agricultural capital investment, which in turn leads to a decline in land use efficiency. In view of this, this paper puts forward suggestions in terms of strengthening ecological monitoring and control, increasing support for agricultural production, and guiding rational social network communication, providing theoretical support and practical guidance for alleviating human–elephant conflict and improving farmers’ land resource use efficiency. Full article

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making. Full article

Rice brown spot disease, caused by Bipolaris oryzae, is a significant fungal disease that poses a major threat to global rice production. Despite its widespread impact, genomic studies of B. oryzae remain limited, particularly those involving high-quality genomic data. In this study, we performed whole-genome sequencing of the B. oryzae strain RBD1, which was isolated from the demonstration field for upland rice cultivation in Haozhiba Village, Lancang County, Pu’er City, Yunnan Province, China, using a combination of second-generation Illumina sequencing and third-generation Single-Molecule Real-Time (SMRT) sequencing. The assembled genome was 37.5 Mb in size with a G + C content of 49.39%, containing 42 contigs with a contig N50 of 2.0 Mb. Genomic analysis identified genes related to carbon, nitrogen, and lipid metabolism, highlighting the strain’s metabolic flexibility under diverse environmental conditions and host interactions. Additionally, we identified pathogenicity-related genes involved in MAPK signaling, G protein signaling, and oxidative stress responses. Under 1.2 M sorbitol-induced osmotic stress, we observed significant differences in growth responses between RBD1 and the rice blast fungus Magnaporthe oryzae H7. Transcriptomic analysis using Illumina sequencing revealed that RBD1 responds to osmotic stress by enhancing carbohydrate metabolism, fatty acid degradation, and amino acid synthesis, while H7 primarily relies on protein synthesis to enhance growth tolerance. This study provides a valuable foundation for understanding the pathogenic mechanisms of rice brown spot and future disease control strategies. Full article

Traditional methods for estimating tea yield mainly rely on manual sampling surveys and empirical estimation, which are labor-intensive and time-consuming. Accurately estimating fresh tea production in different seasons has become a challenging task. It is possible to estimate the seasonal yield of tea at the field scale by using the spatial resolution of 10 m, 5-day revisit period and rich spectral information of Sentinel-2 imagery. This study integrated Sentinel-2 images and uncrewed aerial vehicle (UAV) RGB imagery to develop six regression models at the field scale, which were employed for the estimation of seasonal and annual fresh tea yields of the Yunlong Tea Cooperatives in Yixiang Town, Pu’er City, China. Firstly, we gathered fresh tea production data from 133 farmers in the cooperative over the past five years and obtained UAV RGB and Sentinel-2 imagery. Secondly, 23 spectral features were extracted from Sentinel-2 images. Based on the UAV images, the parcel of each farmer was positioned and three topographic features of slope, aspect, and elevation were extracted. Subsequently, these 26 features were screened using the random forest algorithm and Pearson correlation analysis. Thirdly, we applied six different regression algorithms to establish fresh tea yield models for each season and evaluated their estimation accuracy. The results showed that random forest regression models were the optimal choice for estimating spring and summer yields, with the spring model achieving an R² value of 0.45, an RMSE of 40.38 kg/acre, and an rRMSE of 40.79%. Similarly, the summer model achieved an R² value of 0.5, an RMSE of 78.46 kg/acre, and an rRMSE of 39.81%. For autumn and annual yield estimation, voting regression models demonstrated superior performance, with the autumn model achieving an R² value of 0.42, an RMSE of 70.6 kg/acre, and an rRMSE of 39.77%, and the annual model attained an R² value of 0.47, an RMSE of 168.7 kg/acre, and an rRMSE of 34.62%. This study provides a promising new method for estimating fresh tea yield in different seasons at the field scale. Full article

As the global pursuit of sustainable agricultural practices continues, organic farming is gaining increasing attention. In Pu’er, one of China’s major tea-producing regions, the factors influencing tea farmers’ willingness to adopt organic agriculture have not yet been fully studied. This study integrates the diffusion of innovations theory and the theory of planned behavior, using field surveys to thoroughly analyze the key factors and reasons affecting tea farmers in Pu’er in adopting organic farming practices. The findings indicate that perceptions of the economic benefits of organic farming are the primary drivers of farmers’ willingness to adopt. Experience with organic agriculture training and positive views on environmental and health benefits also significantly enhance the willingness to adopt organic farming. Contrary to common assumptions, education level, age, and household income have minimal influence on adoption willingness. However, low-income families that rely on tea cultivation are more inclined to adopt organic farming. Policymakers should prioritize economic incentives, strengthen training support, and enhance the promotion of the benefits of organic agriculture, while simplifying certification processes and expanding market channels to facilitate the transition of tea farmers to organic agriculture. This study offers insights into the sustainable tea industry and organic farming promotion. Full article

Green space is not always equitably located in cities, and the attractiveness of green space varies, leaving some residents with easy access to high-quality parks and others with little or no access or access to under-maintained parks. To remedy these inequities, this study identified attractive and well-utilized recreational green spaces and developed a model to measure the likelihood of using these recreational green spaces (PSG). The goal was to reduce the travel time and cost of walking or using public transportation to get to green spaces and to design all green spaces to be attractive. The data come from the perspective of the city’s public transportation system and residents’ personal choices. First, the attractiveness of recreational green spaces was calculated from big data on the geolocation of cell phones, measuring the level of provision of recreational green spaces and the trip rates of urban residents. After that, the travel cost to reach recreational green space in residential areas was calculated according to residents’ travel habits. Finally, the probability of all recreational green spaces in the city being used was calculated by combining the population size of residential areas. Taking Pu’er City in China as an example, the attractiveness and utilization rates of recreational green spaces were calculated by PSG, and the results of the study showed that the probability of residents choosing to use the recreational green spaces that are closer to the residential area, with a larger population capacity, and with a higher attractiveness is the highest. The results of the study help promote equitable access to health and socialization opportunities for individuals and communities, thereby promoting environmental justice to help mitigate and respond to climate change. Full article

China’s agricultural sector faces significant challenges, including fragmented farming practices, limited farmer knowledge of sustainable production, and outdated pest control technologies. These issues result in improper fertilization, pesticide application, and disposal of agricultural inputs, contributing to agricultural non-point source pollution and hindering the transition to a green economy. Thus, promoting green production behavior among farmers is critical for achieving carbon peaking, carbon neutrality, and harmonious coexistence between humans and nature. However, the existing literature on this topic is still relatively scarce. This study aims to investigate the impact of farmers’ cognition on their green production behavior (GPB). Considering the role of policy, this study also examines the moderating effect of government regulation in this relationship. An analysis of 306 survey responses from tea farmers in Jingmai Mountain, Pu’er City, Yunnan Province, reveals that farmers’ cognition exerts a significant and positive impact on GPB. Social norms and personal norms serve as chain mediators in the relationship between farmers’ cognition and GPB. Moreover, government regulation moderates the influence of farmers’ cognition on social norms, further amplifying its impact on them. This study advances the theoretical understanding of farmers’ behavior and offers practical insights for fostering the sustainable development of the tea industry. Full article

Forest fires often cause many casualties and property losses, and it is important to explore the time and space laws of forest fires and the influencing factors. The present study used the cities (prefectures) crossed by the Tropic of Cancer (Yunnan section) as the study area. Based on burned land data, a combination of natural factors, such as climate, topography, vegetation, and human activities, such as distance from settlements and population density, a binary logistic regression model, and a boosted regression tree model, were used to analyze the temporal–spatial characteristics and influencing factors of forest fires in 2000 to 2020. The following results were obtained: (1) During 2000–2020, the overall forest fire area in the study area showed a trend of fluctuating decline. The high incidence period of forest fires occurred in 2010. After 2010, the forest fire area in the study area was greatly reduced. (2) The forest fire area in the study area was greater in the east and less in the west. The forest fire areas in Wenshan Prefecture and Honghe Prefecture in the east were larger, accounting for 68%, and the forest fire areas in Pu’er City, Lincang City, and Yuxi City in the west were smaller, accounting for only 32%. (3) The contribution rate of the average precipitation and average temperature factors ranked in the top two in the two driving force analysis models, which indicated that precipitation and temperature had a significant effect on the incidence of forest fires in the study area. Full article

This article, based on the sustainable livelihood framework and survey data from 1264 households in Xishuangbanna Dai Autonomous Prefecture, Puer City, and Lincang City in Yunnan Province, China, analyzes the impact mechanism of livelihood capital on the production input behavior of farmers affected by Asian elephant damage and the moderating effect of Asian elephant damage on this process using ordinary least squares (OLS) models. The study finds the following: (1) Asian elephant damage has a significant negative effect on farmers’ production input, meaning that as the severity of Asian elephant damage increases, farmers reduce their input into agricultural production factors. (2) Livelihood capital has a significant positive effect on farmers’ production input, and both the increment and stock of livelihood capital promote an increase in farmers’ production input. (3) Asian elephant damage strengthens the influence of livelihood capital on farmers’ inputs of agricultural production factors. Based on these findings, four recommendations are proposed: emphasizing the cultivation and enhancement of farmers’ livelihood capital, improving strategies for managing and preventing wildlife damage, optimizing the economic compensation mechanism for human–wildlife conflicts, and adhering to sustainable development and resource allocation. These recommendations aim to enhance wildlife conservation and management policies, strengthen farmers’ risk-coping capabilities, and ensure the sustainability of agricultural production and livelihoods. Full article

Southwestern China is an area heavily affected by forest fires, having a complex combination of fire sources and a high degree of human interference. The region is characterized by karst topography and a mixture of agricultural and forested areas, as well as diverse and dynamic mountainous terrain. Analyzing the driving factors behind forest fire occurrences in this area and conducting fire risk zoning are of significant importance in terms of implementing effective forest fire management. The Light Gradient Boosting Machine (LightGBM) model offers advantages in terms of efficiency, low memory usage, accuracy, scalability, and robustness, making it a powerful predictive algorithm that can handle large-scale data and complex problems. In this study, we used nearly 20 years of forest fire data in Southwestern China as the data source. Using mathematical statistics and kernel density analysis, we studied the spatiotemporal distribution characteristics of forest fires in Southwestern China. Considering 16 variables, including climate, vegetation, human factors, and topography, we employed the LightGBM model to predict and zone forest fire occurrences in Southwestern China. The results indicated the following conclusions: (i) Forest fires in Southwestern China are primarily concentrated in certain areas of Sichuan Province (such as Liangshan Yi Autonomous Prefecture and Panzhihua City), Guizhou Province (such as Qiannan Buyi and Miao Autonomous Prefecture), Yunnan Province (such as Puer City, Xishuangbanna Dai Autonomous Prefecture, and Wenshan Zhuang and Miao Autonomous Prefecture), and Chongqing Municipality. (ii) In terms of seasonality, forest fires are most frequent during the spring and winter, followed by the autumn and summer. (iii) The LightGBM forest fire prediction model yielded good results, having a training set accuracy of 83.088080%, a precision of 81.272437%, a recall of 88.760399%, an F1 score of 84.851539%, and an AUC of 91.317430%. The testing set accuracy was 79.987694%, precision was 78.541074%, recall was 85.978470%, F1 score was 82.091662%, and AUC was 87.977684%. These findings demonstrate the effectiveness of the LightGBM model in predicting forest fires in Southwest China, providing valuable insights regarding forest fire management and prevention efforts in the area. Full article

In recent years, the issue of “human–elephant” conflict in the south of the Yunnan Province, China has been escalating and poses a severe threat to the livelihoods of local residents. To address this problem, this study utilized survey data from farmers in Pu’er City and villages in Xishuangbanna Prefecture, Yunnan Province. By employing land input–output analysis and spatial analysis methods, this study aims to uncover the land use transition in the research area over the past three decades and identify the driving mechanism behind this transition. The findings of this research can provide valuable guidance for reducing regional conflicts between humans and wild animals, as well as improving the livelihoods of farmers. Research indicates that farmers in the study area have significantly transformed their land use practices. The per capita arable land area has increased, and traditional grain crops are being replaced with economically profitable crops such as rubber. Rubber is the predominant crop in the conflict-prone “human–elephant” core region, while other economic crops dominate the peripheral region. The overall land use index has risen, with a greater diversity and stability in land use structure. However, the input–output efficiency of cultivated land in the “human–elephant” core region remains low, leading to a lower comprehensive land use index than that of the peripheral region. The land use transition is influenced by several factors, including socio-economic development, changes in crop comparative benefits, and the activities of wild Asian elephants. Frequent crop destruction by elephants, which results in damage to farmers’ livelihoods, is the primary cause of land use changes in “human–elephant” conflict areas. Ultimately, this conflict stems from the competition for regional land resources between humans and elephants, as humans dominate production space while elephants dominate ecological space. Local governments should optimize the layout of regional production and ecological spaces to alleviate these conflicts while also regulating circulation markets and improving farmers’ land output levels. Full article

The human–elephant conflict is a current issue that receives global attention and occurs in all elephant-distribution countries. This paper focuses on Pu′er and Xishuangbanna cities in the distribution area of Asian elephants in Yunnan Province. Based on two case studies, we collect basic information from local farmers regarding the severity of the damage caused by Asian elephants and the impact this has on their psychology. Based on the Logit model and modulation effect, we analyze the impact farmers’ livelihood capital on how they choose livelihood strategies in the distribution area and whether damage caused by Asian elephants and general conflict conditions can regulate this impact. The results show that the damage caused by Asian elephants and general conflict conditions is serious in the distribution area, but the number of farmers who choose to change their livelihood strategies is small. The damage caused by Asian elephants has a different modulation effect on farmers’ livelihood capital. In the future, we should be more considerate of the livelihood capital accumulation of local farmers in the process of protecting Asian elephants, with a view towards maintaining and improving the livelihoods of farmers. Full article

Excessive intake of high-caffeine tea will induce health-related risk. Therefore, breeding and cultivating tea cultivars with less caffeine is a feasible way to control daily caffeine intake. Cocoa tea (Camellia ptilophylla Chang) is a wild tea plant which grows leaves with little or no caffeine. However, the vegetative propagation of cocoa tea plants is difficult due to challenges with rooting. Whether natural seeds collected from wild cocoa tea plants can be used to produce less-caffeinated tea remains unknown, because research on the separation of traits among the seed progeny population is lacking. The present study was set to investigate the variation of caffeine and other chemical compositions in seed-propagated plant individuals using colorimetric and HPLC methods. It shows that there were great differences in chemical composition among the seed-propagated population of wild cocoa tea plants, among which some individuals possessed caffeine contents as high as those of normal cultivated tea cultivars (C. sinensis), suggesting that the naturally seed-propagated cocoa tea seedlings are not suitable for directly cultivating leaf materials to produce low-caffeine tea. Therefore, the cocoa tea plants used for harvesting seeds for growing low-caffeine tea plants should be isolated in order to prevent their hybridization with normal cultivated C. sinensis plants. Interestingly, the leaves of cocoa tea seedlings contained high levels of gallocatechin gallate (GCG) and would be a good source of leaf materials for extracting more stable antioxidant, because GCG is a more stable antioxidant than epigallocatechin gallate (EGCG), the dominant component of catechins in normal cultivated tea cultivars. Some plant individuals which contained low levels of caffeine along with high levels of amino acids and medium levels of catechins, are considered to be promising for further screening of less-caffeinated green tea cultivars. Full article