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Hilly and mountainous areas are weak places for the development of agricultural mechanization in China. The way to improve the utilization rate of small agricultural machinery widely used in hilly and mountainous areas is of positive significance for optimizing resource allocation efficiency of agricultural production and ensuring food security supply. Taking microtillers as a representative tool, this study systematically analyzed the main factors affecting the utilization rate of small agricultural machines and its influencing mechanism. Then, based on the survey data of 4905 farmers in 100 counties in 10 hilly and mountainous provinces of China, empirical analysis was carried out by some econometric models, such as censored regression and the mediating effect model. Results show the following.: (1) Among farmers in hilly and mountainous areas, the average use time of each microtiller is 218.41 h per year. (2) Age, social identity, terrain conditions, crop types, land area, the number of microtillers, the number of large tractors, and the machinery purchase subsidy policy are the significant factors affecting the utilization rate of microtillers. (3) The increase of cultivated land area not only directly improves the utilization rate of microtillers, but also indirectly improves the utilization rate of microtillers due to the increase in quantity.

Quantitatively revealing the response of carbon stocks to land use change (LUCC) and analyzing the vulnerability of ecosystem carbon stock (ECS) services are of great significance for maintaining the carbon cycle and ecological security. For this study, China’s Guizhou Province was the study area. Land use data in 2000, 2010, and 2020 were selected to explore the impacts of LUCC on carbon stocks in multiple scenarios by combining the PLUS and InVEST models and then analyzing the vulnerability of ECS services. The results show that forest land plays an important role in improving ECS services in karst plateau mountainous areas. In 2000–2020, forest land expansion offset the carbon stock reduced by the expansion of built-up land, greatly improving the regional ECS function. Following the natural trend (NT), the total carbon stock in Guizhou Province will decrease by 1.86 Tg; however, under ecological protection (EP) measures, the ECS service performs a positive function for LUCC. Focusing on socioeconomic development (ED) will increase the vulnerability of the regional ECS service. In the future, the forest land area size should be increased, and built-up land should be restricted to better improve the service function of ECS in karst plateau mountainous areas.

The rural settlements in poverty-stricken mountainous areas are the \"living fossils\" of an economic society with the characteristics of spatial dispersion and are slowly changing. Spatial agglomeration is the development direction of rural settlements. In-depth exploration of the spatial agglomeration characteristics and influencing factors of rural settlements in poverty-stricken mountainous areas is a way to provide a basis for rural settlement restructuring. We selected Pengshui County, a national poverty-stricken county in the southwestern mountainous area of China, as the research area. Spatial buffer and kernel density analysis were used to analyze the agglomeration characteristics of rural settlements and influencing factors. The results show that: (1) The rural settlements are small in scale and the space is evenly dispersed. 55.63% of the rural settlements’ sizes are less than 1000 m2, 84.15% of the rural settlements’ sizes are less than 2500 m2, and 92.81% of the rural settlements are within 200 m. (2) The elevation and slope of topographic factors have a significant agglomeration effect on rural settlements. However, the slope direction has no agglomeration effect. 85.41% of rural settlements (52.75% of rural settlements are gathered between 400 and 800 m above sea level) are gathered at an altitude of 1000 m or less, and 77.59% of rural settlements are gathered with a slope of 6~25°. Additionally, there are few rural settlements with a slope of 0~2°. Moreover, the distribution of residential areas has no agglomeration effect on rural settlements. (3) The cultivated land exerts the most significant effect on rural settlements followed by roads and water sources, while the role of urban land is weak. 99.48% of rural settlements are concentrated in the 100 m area of cultivated land. Therefore, in the poverty-stricken mountainous areas in the southwestern mountainous areas of China, convenient farming is the primary condition for production and living. Rural settlements are highly correlated with cultivated land. Rural settlements are scattered and concentrated with the scattered cultivated land. The rural settlements were leaded by the distribution of cultivated land. Less high-quality cultivated land with less slope were occupied or not by rural residential areas’ people.

In order to meet the terrain adaptability, flexibility, and stability requirement of the agricultural power chassis when driving in complex terrain and narrow fields in hilly and mountainous areas, an arch waist power chassis suitable for a narrow plot with strong ground adaptability is designed. On the undulating road surface, the longitudinal pitch of the track can be used for active and passive ground copying to exert the maximum driving force. Through the theoretical analysis of the whole machine, it is obtained that the minimum turning radius of the chassis is 568.5 mm between the two rear wheels, which is about 34.12% of the length of the whole machine. The longitudinal uphill slip angle αφ is 26.9–42.84°, and the longitudinal downhill slip angle αφ′ is 29.28–43.7°. Finally, the performance test of the whole machine is carried out. The test results show that the arch waist power chassis has a minimum turning radius of 1230.7 mm in field driving, which has good steering maneuverability and can adapt to the narrow operating environment in hilly and mountainous areas; the longitudinal downhill slip angle is 32°, and the longitudinal uphill slip angle is 28°. The longitudinal downhill slip angle is about 1.14 times the longitudinal uphill slip angle, and the lateral slope slip angle is 27°, which meets the requirements for stable operation in hilly and mountainous terrain.

Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data’s positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area’s road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods.

Accurate identification of cultivated land in plateau and mountainous regions remains challenging due to complex terrain and the fragmented, small-scale distribution of farmland. This study develops a high-precision cropland identification model tailored to such environments, aiming to advance precision agriculture and support the scientific planning and refined management of agricultural resources. Taking Xundian County, Yunnan Province, as a case study, multispectral, synthetic aperture radar (SAR), topographic, texture, and time-series features were integrated to construct a comprehensive multi-source feature space. A baseline land use map was generated by fusing datasets from the European Space Agency (ESA), the Environmental Systems Research Institute (ESRI), and the China Resource and Environment Data Cloud (CRLC). Using 4000 randomly selected sample points, five machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Tabular Multiple Prediction (TABM), XGBoost, and the NSGA-II optimized XGBoost (NSGA-II-XGBoost)—were compared for cropland identification. Results show that the NSGA-II-XGBoost model consistently achieved superior performance in classification accuracy, stability, and adaptability, reaching an overall accuracy of 95.75%, a Kappa coefficient of 0.91, a recall of 0.96, and an F1-score of 0.96. These findings demonstrate the strong capability of the NSGA-II-XGBoost model for cropland mapping under complex topographic conditions, providing a robust technical framework and methodological reference for farmland protection and natural resource classification in other mountainous regions.

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area.

China has implemented the world's largest‐ever vegetation restoration program in marginal mountainous areas to sustain life on land. However, land competition between the demand for grain and the need for green has threatened sustainable vegetation restoration. Here, focusing on China's marginal mountainous areas with the highest density of slope cropland, we explore the optimal solution in the trade‐offs between green and grain. We find that current vegetation restoration strategies are not sufficiently optimized, which may threaten the survival and development of local farmers and in turn destroy existing vegetation restoration achievements. Through adjusting vegetation restoration objectives carefully tailored to local conditions, the population experiencing grain shortages can be greatly reduced by 51–66% (from 18.26 million to 6.29–8.90 million) compared with the current scheme. The optimal design will alleviate the conflict between grain and green, thereby promoting sustainable ecological restoration in China. Our research provides an important reference for the world's mountainous areas to achieve a win‐win situation between green and grain. Plain Language Summary Vegetation restoration in China has made remarkable achievements in recent years. However, the sustainability of these vegetation restoration programs has been questioned and challenged. Combined with spatial statistics and scenario analysis for analyzing the trade‐offs between green and grain, we find that the current vegetation restoration strategies in China's mountainous areas are not sufficiently optimized, which may affect the sustainability of vegetation restoration programs. Vegetation restoration strategies adapted to local conditions can reduce the risk of grain shortage for 9.30–11.97 million farmers, and contribute to a more balanced development and the sustainability of vegetation restoration in mountainous areas. Key Points Chinese farmers rely heavily on slope cropland and self‐sufficient farming in marginal mountainous areas Current vegetation restoration strategies are not sufficiently optimized, which may affect farmers' livelihood in mountainous areas Planning tailored to local conditions can alleviate the conflict between grain and green, and promote sustainable vegetation restoration

This study aims to analyze the influence of neighborhood effects (NE) on tea farmers' intention to convert from traditional to organic farming in the mountainous areas of northern Vietnam. It differs from previous studies in two aspects. First, we combine the theory of planned behavior and the theory of herd behavior to explain farmers' intention to convert from traditional to organic farming, focusing on the impact of the NE. Secondly, to measure NE, we use a combination of questionnaires and methods of measuring herd behavior by McCartney and Shah. Using the generalized structural equation modeling and data collected from 263 tea farmers in Thai Nguyen, we found that NE has a positive and direct significant effect on farmers' intention to convert to organic tea production in the case where neighbors both live nearby and have a close relationship with the subject. In addition, it indirectly impacts farmers' conversion intention through attitude, subjective norms and perceived behavior control. To encourage tea farmers to convert to organic farming, policymakers and extension workers should take advantage of the NE to increase farmers' confidence about the benefits and the possibility of successful organic farming.

The power industry is the primary energy through the power generation equipment into electricity, and then through the transmission, transformation and distribution system to supply users as energy as the industrial sector, for the industrial and national economy and other sectors to provide basic power, is the first sector of national economic development[1]. As an important link to ensure the normal operation of the power industry, the importance of the power transmission system is self-evident[2]. The transmission tower, as the cornerstone to ensure the normal operation of the transmission system, has a large construction workload and faces problems such as complex construction environment and construction safety risks. Therefore, how to ensure the safe construction of transmission line in complex terrain is of great significance to power system construction. At present, in the construction of transmission tower, the suspension pole group tower and the whole lifting tower are the two main tower construction methods widely used. However, the pylon group tower has a great safety risk[3-4]. The State Grid has been trying to optimise the suspension of the tower. Secondly, due to the inconvenience of transportation, it is difficult for heavy lifting equipment to reach the construction site, which makes it difficult to promote the whole tower scheme using heavy lifting equipment in the mountainous terrain. In addition, the current construction of the tower group lacks scientific calculation and analysis of the force and deformation of the tower and key construction components, and there are hidden dangers such as overload and excessive deformation. Therefore, a new type of tower formation is needed to avoid the risk of personnel working at high altitudes and problems such as the inability of large heavy equipment to reach the construction site in mountainous areas[5]. In this paper, simulation technology is studied against the background of integral tower technology based on airship.