The alpine grassland ecosystem in the Source Region of the Yellow River (SRYR) faces the dual pressures of ecological protection and economic development. Its ecological fragility and climate sensitivity make local animal husbandry susceptible to meteorological disasters. To overcome adverse selection and moral hazard in traditional animal husbandry insurance, this study integrates 963 field sampling observation data, over 400 valid herdsmen survey data, and long-term environmental time series variables. A random forest model (R2 = 0.59, RMSE = 65.84 g/m2, superior to the artificial neural network in this paper) was used to estimate grass yield. Hodrick–Prescott (HP) filtering was used to separate meteorological yield per unit area and derive yield loss rate. A joint distribution model of meteorological indicators and loss rate was constructed using a Copula function to capture tail-dependent structures, providing a basis for determining trigger thresholds and actuarial pricing of pure insurance premiums. The study reveals the transmission mechanism of climate disasters to feeding costs and designs regional drought and snow disaster index insurance. The compensation standard is based on meteorological indicators falling below the trigger threshold and a yield reduction rate greater than 5%. Using 10,000 Monte Carlo simulations, the drought premium rates for zones I-IV are determined to be 2.03–6.03%, and the snow premium rates to be 2.25–5.42%, corresponding to a premium of RMB 5.21–9.61 per mu for drought and RMB 5.78–8.64 per mu for snow. This design reduces basis risk through zoning and composite triggering, providing a scientific tool for climate risk management in alpine grasslands.

The source region of the Yellow River (SRYR), known as the "Water Tower of China", is not only crucial for sustainability of alpine grassland and wetland ecosystems, but has significant implications for water quality sediment deposition in the river's middle and lower reaches. However, complex topography, climate, and diverse soil erosion types limit the applicability of traditional estimation methods. This study developed a tailored compound erosion prediction model for the SRYR to quantify erosion rates and identify their driving mechanisms. A dataset of 537 soil erosion rates from 137Cs and 33 conditioning variables were used to construct optimal model. Two variable selection methods-genetic algorithm (GA) and least absolute shrinkage and lection operator (LASSO)-were applied alongside three machine learning algorithms: categorical boosting (CatBoost), random forest (RF), and k-nearest neighbors (KNN). Geographical detectors were used to identify erosion drivers. Key findings include: (1) The GA-CatBoost model outperformed others (Rtest 2 = 0.51). Based optimal model, the estimated annual soil erosion rate (2001-2022) in the SRYR was 20.21 t center dot ha-1 center dot a-1, with annual erosion of 238.80 x 106 t center dot a-1. (2) Spatial analysis revealed high erosion in the northwest, low in southeast, and mixed patterns in the central region, with 78.28 % of the SRYR exhibiting improvement 2001-2022. (3) Precipitation and NDVI were identified as the dominant driving factors mitigating soil erosion the SRYR. These findings demonstrate the effectiveness of ecological restoration efforts in the SRYR, providing empirical evidence for targeted soil and water conservation strategies. Future studies could enhance model curacy by diversifying sampling, measuring erosion rates during shorter time windows and using higher lution input data.

Soil organic carbon (SOC) plays a vital role in regional carbon cycling and ecosystem services. However, previous studies have primarily focused on spatial patterns and environmental drivers, with limited attention to long-term observations, underlying mechanisms, and large-scale modeling. In this study, we collected surface soil samples (0–20 cm) and integrated topography, soil physicochemical properties, climate, vegetation, and MODIS remote sensing data to develop 16 SOC prediction models using linear regression and machine learning approaches. SOC was significantly correlated with latitude, mean annual temperature, and precipitation and negatively associated with several remote sensing indices. The LASSO-selected variable set combined with a support vector machine (SVM) achieved the highest predictive accuracy (R2 = 0.53, RMSE = 36.19). From 2001 to 2020, the mean SOC stock in the Yellow River source region was estimated at 1683.98 g C/m2, showing higher values in the southeast and lower values in the northwest. Alpine meadow exhibited the highest total stock due to its extensive coverage, whereas the cold temperate wet coniferous forest had higher mean content and unit area value, indicating strong carbon sequestration potential. This study identifies key SOC drivers and mechanisms, provides quantitative estimates of regional SOC content and stock, and offers a scientific basis for grassland carbon management and large-scale digital soil mapping.

Alfalfa (Medicago sativa L.) plays a crucial role in the revitalization of the dairy industry and grassland agriculture in China. However, regional differences in economic and environmental performance have not been adequately specified or quantified. This study compares alfalfa production in Wuhe County (Southern China) and Ar Horqin Banner (Northern China) by integrating cost–benefit analysis (CBA) with life cycle assessment (LCA). Field data from 22 enterprises were analyzed using one ton of alfalfa hay and a net profit of CNY 10,000 as functional units, over a three-year evaluation period (2017–2019). The assessment encompassed four impact categories: primary energy demand (PED), global warming potential (GWP), acidification potential (AP), and water use (WU). The northern case systems exhibited 67.45% higher production costs but 96.99% greater profitability per ton compared to the southern case, alongside 2.13 × 10−2 greater environmental impact. Conversely, the southern case systems were less profitable and demonstrated an 18.6% higher environmental impact per CNY 10,000 net profit compared to the northern case. Regional environmental hotspots differed: fertilizer use dominated impact in the south, whereas irrigation and electricity consumption drove burdens in the north. To facilitate a sustainable transition, policymakers should implement region-specific support measures, such as ecological incentives and crop rotation schemes for the south, and water-saving technologies along with renewable energy integration for the north. Farmers and enterprises are encouraged to adopt precision input strategies and climate risk management tools, while researchers should focus on advancing adaptive breeding techniques and optimizing resource utilization. The development of a unified system that integrates economic and environmental metrics is crucial for enabling stakeholders to drive the sustainable transformation of alfalfa production.

Based on the food equivalent unit (FEU), this article analyzed Chinese food consumption patterns, spatial mismatch, and production potential to explore agricultural reform strategies. Assessing production–demand mismatch involved the spatial mismatch model, drawing data from statistical yearbooks. Calculations of food production potential utilized the CASA model and the Thornthwaite Memorial model, with net primary productivity (NPP) derived from remote sensing data as indicators. The results showed that livestock product consumption is on the rise, and the spatial mismatch index for herbivorous livestock products was the largest, ranging from 22.81 to 98.12 in 2019. The mismatched degree distribution of rations and food-consuming livestock products showed a trend of increasing on both sides, with the Hu Huanyong line as the center line. Production factors played a predominant role in food production-to-demand mismatch. Climatic productivity and actual productivity decreased from the southeast to northwest in space in 2019, and human activities significantly impacted productivity. When grassland agriculture is pursued as the adjustment orientation, the production potential can reach up to 4540.76 × 107 kg·FEU. Moreover, a grassland agriculture plan was devised, prioritizing its development in the developed southern regions.

Wheat grain production is a vital component of the food supply produced by smallholder farms but faces significant threats from climate change. This study evaluated eight environmental impacts of wheat production using life cycle assessment based on survey data from 274 households, then built random forest models with 21 input features to contrast the environmental responses of different farming practices across three shared socioeconomic pathways (SSPs), spanning from 2024 to 2100. The results indicate significant environmental repercussions. Compared to the baseline period of 2018-2020, a similar upward trend in environmental impacts is observed, showing an average annual growth rate of 5.88 % (ranging from 0.45 to 18.56 %) under the sustainable pathway (SSP119) scenario; 5.90 % (ranging from 1.00 to 18.15 %) for the intermediate development pathway (SSP245); and 6.22 % (ranging from 1.16 to 17.74 %) under the rapid economic development pathway (SSP585). Variation in rainfall is identified as the primary driving factor of the increased environmental impacts, whereas its relationship with rising temperatures is not significant. The results suggest adopting farming practices as a vital strategy for smallholder farms to mitigate climate change impacts. Emphasizing appropriate fertilizer application and straw recycling can significantly reduce the environmental footprint of wheat production. Standardized fertilization could reduce the environmental impact index by 11.10 to 47.83 %, while straw recycling might decrease respiratory inorganics and photochemical oxidant formation potential by over 40 %. Combined, these approaches could lower the impact index by 12.31 to 63.38 %. The findings highlight the importance of adopting enhanced farming practices within smallholder farming systems in the context of climate change. Spotlights: center dot China's smallholder wheat farming is increasingly exposed to climate threats, risking resource depletion and pollution center dot Increasing eco-impacts of smallholder wheat farming could be mitigated by regulated fertilizer use and straw recycling center dot Life cycle assessment combined with machine learning targets future environmental impacts in dynamic climate scenarios center dot The study offers actionable policy advice for sustainable smallholder wheat farming practices center dot Future work may probe machine learning's role in forecasting smallholder production behaviors and farming decision aid

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.

Accurate assessment of grassland soil erosion before and after grazing exclusion and revealing its driving mechanism are the basis of grassland risk management. In this study, the long-term soil erosion in Ningxia grassland was simulated by integrating and calibrating the transport limited sediment delivery (TLSD) function with the revised universal soil loss equation (RUSLE) model. The differential mechanisms of soil loss were explored using the GeoDetector method, and the relative effects of precipitation changes (PC) and human activities (HA) on grassland soil erosion were investigated using double mass curves. The measured sediment discharges from six hydrological stations verified that the RUSLE-TLSD model could reliably simulate water erosion in Ningxia. From 1988 to 2018, the water erosion rate of grassland in Ningxia ranged from 74.98 to 14.98 t⋅ha-1⋅a-1, showing an overall downward trend. July to September is the period with the highest of water erosion. The slope is the dominant factor influencing the spatial distribution of water erosion. After grazing exclusion, the net water erosion rate in Ningxia grassland and sub-regions decreased significantly. The double mass curves results show that human activities were the main driver of net erosion reduction. The focus of water erosion control in Ningxia is to control soil erosion in different terrains and protect grassland with slopes greater than 10°.

In Ningxia, China, information is required to formulate comprehensive plans for ecological restoration and to realize the sustainable development of grassland ecosystems. As such, this study was based on meteorology and remote sensing data related to the grassland ecosystem in Ningxia. We applied the Revised Wind Erosion Equation（RWEQ） to quantitatively evaluate the spatiotemporal variations in soil conservation service functions over a 31-year period before and after grazing prohibition in Ningxia. Our results are as follows: 1) Before（1988 to 2003） and after（2003 to 2018） grazing prohibition, the average soil wind erosion modulus was 5.59 and 1.45 kg·m -2 , respectively. After grazing prohibition, the area of intense soil wind erosion（six levels in total） slightly decreased to 14 281 km~2, the proportion of which increased to73.52%; wind erosion intensity decreased. 2) Before and after grazing prohibition, the average sand-fixing function was 25.49 and 14.30 kg·m -2 , respectively After grazing prohibition, the levels of wind prevention and sand fixation function were mainly classified as medium, covering an area of 8 765 km~2, the proportion of which had increased to 23.65%. 3) From 1998 to 2018, the retention rate of windbreak and sand fixation in Ningxia grassland showed an increasing trend, ranging from 73.2% to 96.7%. Before and after grazing prohibition, the average retention rate of windbreak and sand fixation function was 81.24% and 91.9%, respectively. This finding shows the contribution of grassland to wind prevention and sand fixation in Ningxia was gradually increasing, and the ecological construction project had achieved remarkable results. Therefore,regional governance should be strengthened and differentiated schemes should be implemented in the future.

重新科学地认识宁夏草地类型及其特征对宁夏草地可持续利用与精细化管理至关重要,也对黄河中上游生态安全屏障的构建具有积极意义.本研究运用综合顺序分类系统(CSCS),在ArcGIS平台上制作了宁夏回族自治区CSCS草地类、草地类组分布图,研究其空间分布与类型特征,结果显示:1)宁夏共有12种CSCS草地类,其中,暖温干旱暖温带半荒漠类面积最大,为14786.73 km2,其次是微温干旱温带半荒漠类,面积为8187.36 km2,两种草地类分别占宁夏全区草地面积的48.44％和26.82％.2)宁夏共有4种CSCS草地类组,从北到南依次为半荒漠类组、典型草地类组、温带湿润草地类组和温带森林草地类组,面积逐步减小,分别占据宁夏草原总面积的75.27％、12.39％、8.45％和3.89％.3)根据宁夏草地不同的空间分布特征,将宁夏草地划分为3个片区,分别是宁北半荒漠草地生态保护区、宁中典型草地-温带湿润草地生态经济区和宁南温带森林草地山地景观生产区;并遵循因地制宜的原则,对3个片区分别实行以草定畜、发展草业生态经济和山地-草地景观旅游业等草地管理措施.

The overexploitation of Grasslands without any return-back and compensation is the major cause of degradation and deterioration of the grassland ecosystem. The Subsidy and Incentive System for Grassland Conservation (SISGC) in China aimed to restore grassland ecology by the reduction of overgrazing, promoting carrying capacity, and increasing alternative employment of herders in non-husbandry sectors. However, the ecological response to the SISGC still remains unclear on the national scale. Here, we used systematic sampling, and satellite image time series data revealed a widespread proliferation of major ecological indicators for grasslands, contrasting climate and actual net primary productivity (NPP) before (2004–2010) and after (2011–2017) the implementation of SISGC founded the contributions to policy, as simulated by the Carnegie-Ames-Stanford-Approach (CASA) model. On average, by two-phase comparison, the actual grassland NPP increased by 11.72%. The contribution of policy implementation and climate factors increased grassland NPP by up to 61.14% and 38.86%, respectively, but the response of the NPP growth of various grassland types exhibited divergence, mainly divided into policy-led (contribution rate of 52.28–97.02%) and climate-led (contribution rate of 57.56–96.00%). Hence, the SISGC policies should be renewed for different grassland types.

20世纪90年代以来,生态系统服务流动是目前生态学、经济学和地理学综合研究领域的热点问题.水供给服务流动特征明显,且在众多生态系统服务中占据中心位,是连接生态系统(供给区)与人类生产生活(需求区或受益区)的桥梁,是调节水流量、水循环、水质的重要途径.从供需角度分析水供给服务流动(输送区),模拟流动路径,对于实现水供给服务效益起到关键纽带作用,与此同时,它更强调社会经济与生态环境的协调配合,为建立生态系统和社会经济系统之间的联系具有重要理论和实践意义.因此,针对国内水供给服务空间流动的研究亟待开展.试图构建生态系统水供给服务分析思路框架,识别供给和受益范围,确定服务流动方式、流量及流动路径,探索水供给与需求之间的因果关系.采用文献分析方法系统梳理水供给服务空间流研究进展,分别进行纵向和横向对比.结果表明:(1)按照时间顺序可以分为基础萌芽期、理论发展期、空间关联研究期3个时期;(2)按功能区域可划分为水流供给区、需求区和输送区3个部分,并对比分析各区域定量分析的方法;(3)综合分析结果,国外研究发展快,主要以模型模拟为主.国内水供给服务的研究整体处于起步阶段,停留在生态系统供给与需求的各自时空格局研究层面,水供需服务和空间路径之间的关联缺乏深入研究.

The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.

The water conservation function plays a vital role in the land–water cycle. As the “Chinese water tower”, the headwaters of the Yellow River are of great significance to the safety of the Yellow River basin and even the global ecosystem. Taking the grassland ecosystem in the Yellow River source area as the research object, the InVEST water yield model with modified parameters and the ecological value evaluation of the modified equivalent factor method were used to explore the simulated spatio-temporal changes and the value of grassland water conservation from 2001 to 2020. The results show that: (1) the average total amount of water conservation in the source area is 549 × 108 m3, which is 16% of the runoff in the Yellow River basin, with a growth rate of 7.5 mm/year 1 and a contribution rate of 30%; (2) the total ecological value of grassland water conservation in 2020 is USD 340.03 × 108. The proportion of improved grassland in ecological restoration and management is only 0.51%, while the proportion of original alpine meadow reaches 67% and its ecological function and value are irreplaceable; (3) based on the comprehensive indicators of water conservation capacity, value and importance, Qumalai, Chengduo and Maduo counties are ranked as priority areas for the ecological protection of water resources.

Since the implementation of the grassland ecological protection policy of prohibition grazing on natural grasslands throughout the territory in 2003, the growth of grasslands in Ningxia has improved. This study investigated the spatial differentiation mechanism of normalized vegetation index (NDVI) in Ningxia grasslands from 1988 to 2018, analyzed the relative contributions of climate change (CC) and human activities (HA) to NDVI changes, and predicted the future trend of grassland changes. The results show that except in winter, the annual, seasonal and monthly average values of NDVI after grazing prohibition were higher than those before grazing prohibition. After grazing prohibition, the growth rate decreased by 17.91%, but the degradation rate increased by 3.92%. After grazing prohibition, the proportion of medium coverage increased by 16.15%, mainly in the path of “lower coverage grassland→medium coverage grassland”. The transformation trend was mainly positive, and the ecological construction project has achieved remarkable results. The main factors affecting NDVI differentiation in Ningxia grassland were snow depth, potential evapotranspiration, radiation, and precipitation. After grazing prohibition, the explanatory power of each factor and the interaction between the factors decreased significantly, but the explanatory power of wind speed was greatly improved. After the grazing prohibition, 53.22% of the total area was affected by human activities and climate change. The relative contribution of human activities decreased in NDVI-increased areas but increased in NDVI-decreased areas.

Clarification of the direction of China’s future agricultural structural transformation is important, particularly by analysing the dynamic characteristics of upgrading the food consumption structure, especially the relationship between grain and grass-fed livestock products, and by predicting future food consumption patterns. Based on the food equivalent unit (FEU) and the arable land equivalent unit (ALEU), characteristics of meat consumption elasticity across China and between regions were analysed using the extend linear expenditure system for national meat consumption elasticity and regional changes. Similarly, in addition to the substitution effect of grass-fed livestock products on grain-fed livestock products, the prediction of the future gap in demand for grass-fed livestock products was explored. Results indicated that: (1) from 2005 to 2012, the per-capita food consumption on average has stabilised at 460 kg FEU per year. The consumption of grain-fed livestock products is stable, and that for grass-fed livestock products has increased dramatically since 2000; (2) there is a substitution effect of consumption of grass-fed on consumption of grain-fed livestock products on individual and regional scales; (3) By 2035, the overall demand gap for future grass-fed livestock products in China will be 1.14 × 1010 kg, and the main demand will be in grain farming areas. Based on the aggregated advantage index of each region, the seven grassland ecological-economic regions are divided into three priority groups for grassland agricultural development.

It is of great significance to quantitatively evaluate the ecological degradation and restoration of the Yellow River source area from 2001 to 2017. Net primary productivity (NPP) is the core indicator used to indicate the health of terrestrial ecosystems. Based on the improved CASA (Carnegie-Ames-Stanford Approach) model and MOD17A3 NPP products, 174 measured sample sites were used to validate the NPP simulation results, select the model with higher accuracy as the actual NPP simulation, and using Theil-Sen median trend analysis and Mann-Kendall test to investigate the dynamics of NPP changes. The results are as follows: (1) the accuracy of the CASA model is higher than that of the MOD17A3 NPP product, and it is more suitable for the simulation of the actual NPP in the source area of the Yellow River; (2) the Yellow River Source region has shown a fluctuating upward trend in net primary productivity over the past 17 years, with 50% of the region showing a slight improvement, 36% showing a significant improvement, 13% showing a slight decline, and only 1% showing a significant decline, the average annual growth rate of NPP is 2.6 g C/yr-1. Compared with 2001, the net primary productivity in 2017 increased by 5.24×10 12 g C.

草地综合顺序分类法(comprehensive and sequential classification system of grassland,CSCS)经过60多年的不断探索和完善,已成为具有中国知识产权的唯一的可数量化的草地分类系统.特别是2008年任继周等在Rangeland Journal专门著文推介CSCS,开启了CSCS在国内外研究的新高潮.本研究以CSCS作为关键词从Wed of Science及中国知网等科技论文数据库检索得2008-2020年发表的中英文文献分别为48和29篇.通过系统梳理,获得最新的研究成果如下:1)将CSCS与国际公认的Holdridge Life Zone、BIOME4分类体系在全球尺度上进行对比验证,论证了CSCS在草地类型划分方面的突出优势;2)使用数字高程模型数据的坡度、坡向和坡度变化率等因子修正传统空间插值法,引入海拔、坡度等变量的多元回归和残差分析插值法,有效解决高海拔和复杂地形所带来的气候数据插值误差,提高了CSCS的模拟精度,也为深入广泛的应用提供了方法论依据;3)基于CSCS发生学特征,研究草地对全球气候变化的响应.现已在区域、全国及全球尺度上研究草地生态系统对全球气候变化的响应,为进一步的草地精细化分类管理和相关政策制定提供了数据基础;4)热量状况和水分条件的组合是草原现象和过程的本质的因素,以CSCS为理论框架,用分类指标为参数构建草地第一性生产力(NPP)分类指数模型,该模型不仅揭示草地类型与其净第一性生产力的内在联系,也为进一步研究地带性草地类型的生产潜力、草地净第一性生产力的区域分布和全球分布提供了可能.在区域、全国和全球尺度上的比较验证可知,基于CSCS的草地NPP模型已发展成为草地生态系统第一性生产力评估及碳汇计算的新工具.未来CSCS研究亟待开展的工作主要有:1)完善CSCS亚类及型的定量分类体系;2)通过开发CSCS方法在草地营养载畜量和生态服务价值评估等方面的应用,完善基于CSCS框架的草地精细化管理.

为探究草原生态补奖政策对青藏高原县域草地植被状况的影响,本文以2006—2018年青藏高原主体省份青海西藏牧区68个县域的数据以及通过遥感技术获取的归一化植被指数(normalized difference vegetation index,NDVI)为基础,用NDVI值量化草地植被状况,应用固定效应模型控制不同县之间社会经济因素、气候因素的影响,通过固定效应模型分析草原生态保护补助奖励政策对青藏高原草地植被状况的影响效果.研究结果表明,草原生态补奖政策对草地植被的影响呈现明显的空间异质性,对草地状况好的县的影响大于草地状况差的县,对青海省的影响大于西藏自治区,对牧区县的影响大于半牧区县.草原生态补奖政策对草地保护是有效的,但有效性被其他社会经济和气候因素降低或中和.在未来有关草原保护政策制定的过程中需要考虑与市场和气候条件相关的其他因素可能的抵消作用.

Accurate estimation of the aboveground biomass (AGB) of grassland is a key link in understanding the regional carbon cycle. We used 501 aboveground measurements, 29 environmental variables, and machine learning algorithms to construct and verify a custom model of grassland biomass in the Headwater of the Yellow River (HYR) and selected the random forest model to analyze the temporal and spatial distribution characteristics and dynamic trends of the biomass in the HYR from 2001 to 2020. The research results show that: (1) the random forest model is superior to the other three models (R2val = 0.56, RMSEval = 51.3 g/m2); (2) the aboveground biomass in the HYR decreases spatially from southeast to northwest, and the annual average value and total values are 176.8 g/m2 and 20.73 Tg, respectively; (3) 69.51% of the area has shown an increasing trend and 30.14% of the area showed a downward trend, mainly concentrated in the southeast of Hongyuan County, the northeast of Aba County, and the north of Qumalai County. The research results can provide accurate spatial data and scientific basis for the protection of grassland resources in the HYR.