Drought is the dominant driver of grassland NPP variation (explaining up to 84% of NPP anomalies), with meadow steppe being the most drought–sensitive and desert steppe the most resilient; drought impacts on NPP, hay yield, sheep units, and economic loss amplify linearly with severity (extreme drought causes 2.8× higher Npp loss, 1.1× higher hay loss, and 4.4× higher economic loss than moderate drought). Abstract Drought, one of the most severe natural disasters globally, has inflicted notable impacts on animal husbandry production, yet the current research on drought impact assessment in pastoral systems is plagued by obvious gaps, such as the lack of comprehensive quantitative evaluations integrating grassland ecosystem and livestock production indicators, unclear quantitative relationships between drought severity gradients and multi-level pastoral impacts, and the absence of validated quantitative assessment frameworks linking drought indices with actual pastoral economic losses. To fill these gaps, this study takes Inner Mongolia grasslands as the research area, analyzes the spatiotemporal characteristics of drought and its impacts on grassland net primary productivity (NPP) over the 50-year period from 1961 to 2012, and quantifies the differential impacts of three representative gradient drought events (1974 moderate, 1986 severe, and 1965 extreme) on grassland NPP, standard hay yield, sheep units and livestock economic losses. The long-term analysis shows that drought frequency in the study area decreases with increasing severity, with the typical steppe having the highest drought frequency and a “nine droughts in ten years” pattern in the central and western regions; drought intensity increases westward, and duration extends with rising severity, and its spatial distribution is highly consistent with the east–west precipitation gradient. Drought is the dominant driver of NPP variation, explaining up to 84% of NPP anomalies, with meadow steppe being the most sensitive to drought and desert steppe showing stronger drought resilience due to adaptive traits such as deeper root systems. The assessment of the three representative drought events reveals that drought impacts exhibit a linear amplification effect with severity, with extreme drought causing an average NPP loss 2.8 times greater, hay yield loss 1.1 times greater, and economic loss 4.4 times greater than those caused by moderate drought, and different grassland types show distinct response characteristics to drought of varying severity. The NPP loss spatial distribution is highly consistent with severe drought areas, and sheep unit loss is directly correlated with drought severity. Most importantly, the study validates a robust quantitative assessment framework ( S P I → N P P → h a y y i e l d → s h e e p u n i t s → e c o n o m i c l o s s ) with relative errors of less than 9% compared with historical disaster records, which systematically links drought indices with practical pastoral economic losses. This research clarifies the quantitative relationships between drought and multi-dimensional pastoral impacts, and provides actionable scientific insights for drought risk governance in arid and semi-arid pastoral areas such as Inner Mongolia. 1. Introduction China is a country with frequent droughts, especially in arid and semi-arid regions [ 1, 2, 3, 4]. Current disturbance patterns and global changes significantly impact ecosystem resources and productivity [ 5]. Precipitation serves as a critical regulatory factor for grassland vegetation growth in arid and semi-arid regions [ 6], with drought causing substantial reductions in regional ecosystem carbon storage and carbon fixation capacity [ 7]. As drought conditions worsen, maintaining grassland carbon pools and sinks becomes increasingly challenging due to high spatiotemporal variability and climate fluctuations [ 8, 9, 10]. Key factors influencing vegetation productivity and carbon sequestration include the duration, intensity, and spatial extent of drought events affecting grassland interannual carbon balance [ 11]. Drought impacts exhibit lag effects, with soil properties and precipitation frequency/intensity influencing soil water content for up to two years, potentially exacerbating net ecosystem exchange (NEE) anomalies by as much as 40% [ 5]. Such legacy effects are particularly consequential because severe, sustained droughts can dramatically affect grassland ecosystem carbon cycles, potentially offsetting decades of accumulated carbon storage in a single event [ 12]. Importantly, these impacts vary substantially across grassland types due to inherent differences in ecosystem resistance and resilience [ 13]. In fact, due to the delayed onset of drought, the cumulative effect of water deficit on grassland ecosystems increases with drought duration and intensity [ 14, 15]. Since ecosystems possess inherent adaptability and resistance, drought only significantly impacts them when it exceeds their critical tolerance threshold [ 16, 17]. Mild drought or water deficit demonstrates limited effects on vegetation, potentially yielding positive outcomes or showing no significant differences in vegetation status pre- and post-drought [ 18, 19]. Substantial evidence indicates that ecosystems can endure short-term extreme events or moderate droughts, with varying impacts across different drought severity levels [ 12, 20, 21, 22, 23]. Some studies have observed maintained grassland ecosystem productivity even during localized extreme drought events [ 20, 21, 24]. These phenomena may result from multiple factors: the buffering capacity of grassland soil carbon against extreme events [ 25, 26], vegetation-soil interactions [ 27], interspecies cooperation [ 28, 29], enhanced vegetation water use efficiency during drought under elevated atmospheric CO 2 concentrations [ 30, 31], and increased resistance to extreme drought stress due to recurrent mild drought events [ 32, 33]. However, ecosystem fragility means that as drought severity progresses, the intensifying drought conditions eventually exert impacts exceeding ecosystem tolerance, the so-called “unacceptable impact” threshold [ 34]. Consequently, this study focuses exclusively on medium-to-severe drought effects on grassland ecosystems, excluding mild drought impacts. Drought has exerted significant impacts on socio-ecological systems, garnering worldwide attention [ 35, 36, 37]. Current drought impact assessments primarily focus on agricultural sectors [ 38, 39, 40], including both crop production [ 41, 42, 43, 44] and pastoral systems [ 45, 46, 47, 48]. Regarding crop productivity, Orimoloye focused on assessing agricultural drought impacts on crop production in Free State Province, South Africa. Their results demonstrated significant drought effects on maize and sorghum yields, particularly during 2015 and other drought years that recorded the lowest production levels [ 49]. Mohammed et al. [ 50]. examined the physical properties of agricultural drought, such as intensity, duration, and severity in Hungary from 1961 to 2010 based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), and analyzed the interaction between dryness and crop yield for maize and wheat. Qin et al. [ 51] assessed the drought impact on sugarcane yield across different growth stages in Guangxi, China, from 1995 to 2016, based on crop water requirements and SPEI. Concerning pastoral impacts, extensive research indicates that drought is threatening plant growth and soil nutrients in grassland ecosystems [ 52], altering gross primary production allocation and reducing productivity in widespread pasture grasses [ 53]. For instance, Smith et al. conducted a coordinated distributed experiment to quantify short-term drought impacts on grassland and shrubland ecosystems, demonstrating with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated [ 54]. Lei et al. [ 55] proposed a novel framework for evaluating drought impacts on grassland ecosystems, revealing progressively greater NPP losses with increasing drought severity. Felton and Goldsmith integrated remote sensing-derived gross primary productivity with meteorological data to systematically evaluate the timing and magnitude of drought impacts on carbon uptake across the western US Great Plains grassland biome [ 56]. Gao employed land use and climate data to drive ecosystem process models, quantitatively assessing the effects of land use and climate change on NPP, vegetation carbon storage, soil respiration, and carbon sequestration in agricultural–pastoral transition zones [ 57]. Based on correlation analysis and the coefficient of variation method, Liu et al [ 52] analyzed the cumulative and lag effects of multi-timescale drought on grassland NPP under different climatic zones, altitudes, and water availabilities in Central Asia from 1982 to 2018, and discussed the impact of temporal effects on grassland NPP stability. Most scholars study the impacts of climate change on ecosystems from a functional perspective, analyzing drought effects through comparison with established assessment criteria [ 13]. Currently, few comprehensive quantitative evaluation methods exist to systematically assess drought impacts on both grassland productivity and livestock production, especially concerning the quantitative relationships between pasture NPP variations and livestock output values across different drought severity levels and grassland types. Therefore, this study focuses on utilizing the Standardized Precipitation Index (SPI) and the Biome-BGC model to develop a dynamic assessment model for livestock drought losses. This model quantifies the effects of moderate, severe, and extreme drought events on forage yield, sheep units, and livestock output value. The applicability of this method is further evaluated by incorporating drought loss survey data. Existing studies on drought and its impacts have laid a foundation for understanding drought characteristics, propagation, and ecological/socio-economic effects, but critical gaps remain, leading to unclear mechanisms and limited practical guidance. Most studies employ either the SPI or the SPEI as the sole index and focus on single drought types (e.g., meteorological or hydrological drought) or single impact dimensions (e.g., vegetation productivity or crop yield) [ 51, 58], lacking a holistic analysis of the chain effect from “drought type conversion ( m e t e o r o l o g i c a l → h y d r o l o g i c a l → a g r i c u l t u r a l / p a s t o r a l )” to “multi-level impacts ( e c o s y s t e m → p r o d u c t i o n → l i v e l i h o o d )” [ 59, 60]. For example, studies on drought propagation in semi-arid regions (e.g., eastern Gansu) clarify meteorological-to-hydrological drought conversion but ignore links to pastoral production losses; pastoral impact assessments focus on yield/economic losses but lack connection to upstream drought propagation mechanisms. Compound drought (concurrent drought and heat) studies highlight the increasing frequency in arid/semi-arid regions but fail to reveal how compound stress differs from single drought in impacting grassland–pastoral systems, especially regarding vegetation resilience and livestock adaptation thresholds. At the ecological scale, while studies confirm drought’s impact on vegetation phenology and productivity, the differential responses of diverse grassland types (meadow, typical, and desert steppe) to drought intensity (moderate, severe, and extreme) and duration remain unclear. For instance, the sensitivity of vegetation net primary productivity (NPP) to drought varies across grassland types, but the underlying physiological (e.g., stomatal regulation, carbon allocation) and microbial (e.g., soil organic carbon pool stability) mechanisms remain not fully elucidated. At the socio-ecological scale, the interactive effects of climate (precipitation variation) and human activities (grazing intensity, water use) on drought vulnerability are understudied. Existing research either emphasizes climate-driven drought impacts or isolated human activity effects, lacking analysis of how grazing intensity modulates drought–vegetation–livestock feedbacks under variable precipitation. Critical thresholds for drought impacts are undefined. For example, the minimum drought intensity or duration triggering significant declines in grassland carrying capacity or livestock mortality, and the precipitation variability threshold required to maintain plant diversity in grazed semi-arid grasslands, remain unquantified. Adaptation strategies are generic: Current measures (e.g., afforestation, reservoir construction) lack specificity for different drought types and scales. There is no clear guidance on how to adjust grazing intensity or water allocation based on drought propagation characteristics (e.g., time lag, intensity attenuation) to mitigate pastoral losses. Most studies either analyze long-term drought trends (1961–2014) or single extreme drought events (e.g., 1965 extreme drought in Inner Mongolia) but fail to explore how long-term climate change (e.g., warming, precipitation pattern shifts) modifies the intensity, impact scope and recovery trajectory of extreme droughts in grassland–pastoral systems. For example, whether long-term wetting in northwest China reduces extreme drought impacts, or whether warming amplifies compound drought–heat effects, remains unaddressed. To fill the above gaps, this study aims to: Systematically reveal the multi-scale chain mechanisms of “drought propagation ( m e t e o r o l o g i c a l → h y d r o l o g i c a l → p a s t o r a l )—ecological response (vegetation–livestock)—human adaptation” in semi-arid grassland pastoral systems, quantify critical thresholds of drought impacts under climate–human interaction, and propose targeted adaptation strategies. Break through the limitations of single-dimensional drought research and establish a multi-scale, integrated analytical framework that links drought propagation, ecological responses, and human activities, thereby enriching the theory of drought socio-ecological system impacts. Clarify the differential response mechanisms of diverse grassland types and livestock systems to drought intensity, duration, and compound stress (drought–heat), providing new insights into semi-arid ecosystem resilience and stability. Quantify critical thresholds (e.g., drought intensity triggering a 10% livestock loss, precipitation variability maintaining plant diversity) to provide scientific benchmarks for the early warning of drought risk and grazing management. Propose adaptive strategies tailored to drought propagation characteristics (e.g., adjusting grazing intensity based on the meteorological–hydrological drought time lag) and grassland type, supporting sustainable management of grassland pastoral systems in semi-arid regions. 2. Study Area, Data Sources and Methodology 2.1. Study Area The Inner Mongolia Grassland constitutes the main body of temperate grasslands in Northern China, with its natural grassland area spanning 86.667 million hectares (hm 2), which accounts for approximately 25% of China’s total grassland area, as shown in Figure 1. This vast and boundless grassland extends from east to west, primarily encompassing meadow steppes, such as the Hulunbuir and Horqin Grasslands, typical steppes, including the Xilin Gol and Ulanqab Grasslands, as well as the Ordos Semi-Desert Grassland and the Alxa Desert Steppe. Collectively, these biomes cover 67.5% of the total land area of the Inner Mongolia Autonomous Region, establishing it as one of China’s largest natural ranches; consequently, the region holds an extremely important position in the context of China’s grassland resources and livestock production, playing a crucial role in sustaining regional development and maintaining ecological balance. Grassland is the dominant natural vegetation in Inner Mongolia, exhibiting clear zonality influenced by geomorphology, climate, and soil, as shown in Figure 2. Corresponding to the spatial distribution of chernozem, chestnut soil, and brown calcic soil, the region features temperate meadow steppe, typical steppe, and desert steppe from east to west. Meadow steppe (11% of total grassland; 300–600 mm precipitation) is dominated by Stipa baicalensis and Leymus chinensis. Typical steppe (35%; 200–400 mm) is characterized by xeric perennial herbs such as S. grandis and L. chinensis. Desert steppe (11%; 0–200 mm) mainly consists of more drought-tolerant, dwarfed species such as S. klemenzii. Positioned at the edge of the East Asian monsoon and continental climate zones, Inner Mongolia experiences cold, dry winters and hot, rainy summers, resulting in precipitation that is highly variable annually and unevenly distributed spatially. Under these combined effects, drought is the primary threat to the local grassland ecosystem. Over 80% of the temperate steppe area lies within arid and semi-arid regions, facing frequent, intense, and prolonged droughts. The region has seen an increase in the frequency and severity of droughts in recent years, including a continuous 10-year drought period since 2000. This makes the Inner Mongolia Grassland an ideal field site for studying the impacts of actual drought events. 2.2. Data Sources This study quantitatively evaluates the impact of drought on grassland productivity in Inner Mongolia using an ecological process model and the SPI drought monitoring index. The required data are detailed in Table 1. The daily meteorological inputs for the ecological process model (e.g., Biome-BGC) include Tmax, Tmin, Prcp, VPD, Srad, and Daylen. Monthly precipitation is used for SPI (Standardized Precipitation Index) calculation. The SPI developed by McKee et al. [ 61] was adopted for drought identification. The SPI is more sensitive to short-term rainfall than the Palmer Drought Severity Index (PDSI), making it better suited for monitoring changes in soil moisture. It offers a prompt response to the onset of drought, allowing for earlier drought detection, and exhibits good spatial standardization. This study utilizes daily meteorological data from 40 qualified sites in Inner Mongolia (1960–2009), sourced from the China Meteorological Data Sharing Service Network. Missing shortwave radiation flux density ( Srad) and day length ( Daylen) are estimated using the MT–CLIM (Mountain Microclimate Simulation Model) embedded in Biome-BGC, which requires only daily Prcp, Tmax, and Tmin inputs. Temperature influences physiological rates; precipitation determines soil water potential; and vapor pressure deficit (VPD) affects stomatal conductance and carbon uptake. Additionally, the CN05.1 gridded meteorological dataset (0.25° × 0.25°, 1961–2012) is employed. Soil attribute data (sand, silt, clay content, and depth) are derived from the China Soil Dataset (v1.1, FAO-90 classification). Vegetation type data are extracted from the highly accurate 1:1,000,000 Vegetation Atlas of China. For model calibration and validation, flux observation data and literature-based biomass data from the China Flux Observation Network (ChinaFLUX and COIRAS) are utilized. Key flux sites include the Tongyu Station (meadow steppe), Xilinhot Station (typical steppe), and Sunite Left Banner Station (desert steppe), representing the major grassland types in Inner Mongolia. 2.3. Methodology This study focuses on two newly developed/optimized core methods; existing methods (e.g., Biome-BGC basic simulation, SPI basic calculation) are briefly described: 2.3.1. Localization Optimization of Biome-BGC Model Parameters The Biome-BGC model simulates the daily carbon, water, and nitrogen cycling processes in grassland ecosystems. The formula for calculating net primary productivity is as follows: N P P = G P P − R a (1) In the formula: G P P represents the total primary productivity; R a represents autotrophic respiration. To enhance the model’s simulation accuracy for the temperate grasslands of Inner Mongolia, this study conducted localized calibration and validation of model parameters using field measurement data and flux observation data. Key physiological and ecological parameters were optimized for meadow grasslands, typical grasslands, and desert grasslands, including the leaf carbon-to-nitrogen ratio, specific leaf area (SLA), maximum stomatal conductance, fine root turnover rate, and the proportions of easily decomposable carbon, cellulose, and lignin in litter. 2.3.2. Model Calibration and Validation This study utilized three representative grassland vortex correlation observation stations from the ChinaFLUX network, each representing one of the three major grassland types in the study area, to conduct model calibration and validation. Tunyu Station (Grassland Plain) Xilinhot Station (Typical grassland) Sunit Left Banner Station (Desert Grassland) Calibration data: Total Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET), and Annual Net Primary Productivity (NPP) for the period 2000–2010. Validation data: Independent biomass and NPP values derived from publicly available literature and field measurements. Calibration procedure: The parameters were iteratively adjusted with the objective of minimizing R 2 the root mean square error (RMSE) between simulated and observed values while maximizing the coefficient of determination. First, the model was run using the default parameters, then the key parameters were gradually optimized. The calibrated parameters were then fixed for spatial simulation across the entire Inner Mongolia region. Verification result: The simulated annual NPP showed good agreement with the observed values R 2 > 0.80 , p west) as shown in Figure S4. Furthermore, multi-scale SPI–NPP correlation analysis with lags of 0–12 months revealed distinct lag characteristics: grassland, 1–2 months; typical grassland, 2–3 months; and desert steppe, no significant lag. In general, the drought frequency was higher in the central and western regions than in the eastern regions, exhibiting the characteristic pattern of “nine droughts in ten years” [ 62]. These findings align with Smith’s observations of maximum drought frequency in the northeastern Hulunbuir League and minimum frequency in the western Alxa League [ 63]. 3.2. Quantifying Drought Impacts on Grassland NPP Prior to the quantitative analysis of drought’s impact on NPP, we first established the drought–NPP relationship using the 12-month Standardized Precipitation Index (SPI_12). As shown in Figure 3, the analysis revealed a strong positive correlation between NPP and SPI_12, with 80% of the study area showing high correlation (R > 0.5) and only 20% exhibiting weak correlations—primarily in western desert steppes (characterized by a low NPP of 44–100 g C/m 2/yr) and northeastern typical grasslands (likely temperature-influenced). Steppe-type comparisons showed meadow steppes had the highest NPP-SPI_12 correlation (95.2% of areas with R > 0.5), followed by typical steppes (91.4%), while desert steppes demonstrated significantly lower correlation (36.8%), indicating greater drought tolerance that aligns with field observations. This resilience stems from adaptive traits developed under chronic water stress, including deeper root systems that enhance soil water acquisition in low-precipitation environments, allowing desert steppe vegetation to maintain growth despite moisture limitations (Müller and Bahn, 2022) [ 33]. The coefficient of determination (R2) of annual precipitation deficit for NPP was generally high, peaking at 0.84 ( Figure 4). Overall, 67% of the study area exhibited R2 > 0.5, indicating that drought is the primary driver of regional NPP anomalies. Among steppe types, meadow steppe exhibited the highest maximum R 2 (0.84), with 77.1% of its area showing R 2 > 0.5. Typical steppe followed closely with a maximum R 2 of 0.81 and 77.3% coverage of R 2 > 0.5 areas. In contrast, the desert steppe showed lower drought sensitivity, with a maximum R 2 of 0.79 and only 26.1% of its area exceeding R 2 > 0.5. These results confirm that drought is a critical stress factor for grassland NPP, consistent with findings reported by Zhang et al. (2014) [ 64]. The statistical analysis of the significance levels for the correlation and the coefficients of determination ( Figure 5) revealed that all steppe types exhibited statistically significant drought–NPP relationships at p 27,900 dead livestock in Sunit Right Banner; >64,000 in East Uzhumuqin Banner). Hulunbuir and Tongliao Cities experienced 1.35 million hectares of affected pastureland with 8% livestock mortality. As shown in Figure 8b, the 1986 severe drought caused SSU losses ranging from −0.18 to 1.05 N/hm 2/yr, with maximum reductions concentrated in the northeastern desert steppe, western typical steppe, and the southeastern/northeastern meadow steppe regions of Inner Mongolia. Following spring 1986, large-scale drought conditions developed across the region, characterized by: (1) Ulanqab City’s 15–20 cm dry soil layer, groundwater depletion, and drinking water shortages; (2) Huade County and Shangdu County experiencing near-zero precipitation during April–May; (3) Chifeng City recording 76% below-average precipitation from April to mid-June, exacerbated by high winds accelerating soil moisture loss; and (4) Bayannur City’s Urat grassland receiving 80% less March–May rainfall, affecting 3.8 million hectares and 1.3 million livestock. Although June rainfall temporarily alleviated drought in central-western regions through four precipitation events (June 9–26) in Xilin Gol League, subsequent July–August precipitation west of the Second Tier measured 40–70% below normal, creating compound spring–summer drought effects. Severe impacts included: Bayannur’s Urat grassland vegetation withering post-greening, causing 1.59 million livestock to face critical forage shortages with 51,200 deaths by August’s end; Wulanqab’s Shangdu County, Xinghe County, and Chahar Right Wing Rear Banner reporting 8600 drought-related livestock deaths; Damao Banner recording 29,000 deaths by June; and Siziwang Banner losing 35,000 livestock. Western Hulunbuir City’s Great Khingan Mountains region experienced sustained drought with monthly precipitation deficits of 30–80% (annual precipitation 50–65% below normal), desiccating lakes and rivers, and creating critical water shortages for both vegetation and livestock. As shown in Figure 9, the 1965 extreme drought caused SSU losses ranging from −0.63 to 1.82 SSU/ha/yr, with maximum reductions concentrated in the southeastern desert steppe, southwestern typical steppe, and southeastern meadow steppe regions of Inner Mongolia. During spring–summer 1965, pastoral areas experienced widespread drought conditions, particularly affecting: (1) northern and western Chifeng City (severe spring drought); (2) western regions beyond the Second Tier (severe summer drought); (3) southern Xilin Gol League (delayed grass green-up by 10–15 days); (4) Ordos City (vegetation withering post-green-up); and (5) Otog Banner (year-round drought with complete grassland desiccation and well depletion). The drought impacts included: 2 million livestock deaths across 16 banner counties west of the Second Tier; 112,000 deaths in Ar Horqin Banner (300-day drought causing pasture depletion and disease outbreaks); and regional totals of 8.853 million affected livestock, including 4.763 million drought-related deaths. Based on the national standard for livestock carrying capacity of natural grasslands (NY/T 635-2015), the calculation yields: The 1974 mid-year drought caused a yield loss of—0.16 to 1.03 SSU·hm −2 per unit area. Severe drought in 1986: loss of 18 to 1.05 SSU·hm −2; The 1965 extreme drought caused losses ranging from −0.63 to 1.82 SSU·hm −2. The high-value loss zones in sheep production areas coincide with the centers of severe and extreme drought, indicating that drought directly reduces livestock carrying capacity by decreasing forage availability. 3.6. Quantitative Assessment of Livestock Economic Losses Across Drought Severity Levels According to historical data from “Flood and Drought Disasters in China”, the pastoral areas of Inner Mongolia experienced the most severe drought losses during the 1960s–1970s, with an average annual drought loss of 140 million yuan. The year with the highest drought loss rate was 1965, when the losses accounted for nearly half of that year’s total pastoral output value, reaching a drought loss rate of 49.32%. During the 1970s–1980s, the average annual drought loss was 138 million yuan. By the 1980s–1990s, the average annual drought loss had declined to 107.4 million yuan, with the drought loss rate in 1986 reaching 5.83%. The economic loss was calculated at a constant price of 700 yuan per standard sheep unit in 1980, and compared with the actual situation described in China’s Water and Drought Disasters: In 1974, the simulated loss amounted to RMB 67 million, with a relative error of 6.94%. In 1986, the simulated loss amounted to 135 million yuan, with a relative error of 8.78%. In 1965, the simulated loss amounted to 590 million yuan, with a relative error of 4.24%. All errors were below 9%, demonstrating the reliability and accuracy of the evaluation chain established in this study: S P I → N P P → f o r a g e y i e l d → s h e e p u n i t → e c o n o m i c l o s s The references are specific to the research topic and probably include more recent works in the field of remote sensing. The figures and tables are clear, but some could benefit from clearer explanations, for example, in the legends and units. In this study, livestock economic losses were calculated using a constant 1980 price of 700 yuan per standard sheep unit (SSU), with inflation adjustment based on the Consumer Price Index (CPI, 1980 = 100). The inflation
