Spatial and temporal evolution of forage-livestock balance in the agro-pastoral transition zone of northern China

doi:10.1007/s40333-025-0016-8

Journal of Arid Land

2025, Vol. 17

Issue (6): 754-771 DOI: 10.1007/s40333-025-0016-8 CSTR: 32276.14.JAL.02500168

Review article

Spatial and temporal evolution of forage-livestock balance in the agro-pastoral transition zone of northern China

LIU Huan¹, YAO Yuyan², AI Zemin¹^,^*(

), DANG Xiaohu³, CAO Yong¹, LI Qingqing¹, HOU Mengjia¹, HU Haoli¹, ZHANG Yuanyuan¹, CAO Tian¹

¹College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
²Natural Resources Agency, Sanyuan County, Xianyang 713800, China
³College of Geology and Environment, Xi'an University of Science and Technology, Xi'an 710054, China

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Abstract

Research on grassland carrying capacity (GCC) and forage-livestock balance is of great significance for promoting the harmonious development of human and grassland. However, the lack of understanding of GCC and forage-livestock balance in the agro-pastoral transition zone of northern China has limited the grassland sustainable development. Here, the spatial and temporal characteristics of GCC and forage-livestock balance in the grassland of agro-pastoral transition zone of northern China from 2000 to 2022 were analyzed using meteorological data and remote sensing data. Geographical detectors and geographically weighted regression were also used to identify the driving factors and their interactions with GCC changes. Moreover, future GCC trends were predicted using the Coupled Model Intercomparison Project Phase 6 dataset. Results revealed that: (1) GCC showed an overall upward trend from 2000 to 2022 but with significant inter-annual fluctuations. Its spatial distribution decreased gradually from north to south and from east to west. Precipitation, temperature, and cumulative solar radiation were the main drivers of the inter-annual variation of GCC, and the interaction between precipitation and temperature was the main influencing factor of the spatial distribution of GCC; (2) the forage-livestock balance was in an overloaded state in most years, but its index remained basically stable. Spatially, grazing overloading was mainly distributed in northeastern area and the severe overloading was mainly distributed in northwestern area; and (3) future projections indicated a downward trend in potential GCC. Under shared socioeconomic pathway (SSP)2-4.5 scenario, the potential GCC had a ranged of 1.38×10⁷-1.86×10⁷ standard sheep unit (SHU) and a mean of 1.60×10⁷ SHU. Meanwhile, the potential GCC under SSP5-8.5 scenario had a range of 1.18×10⁷-1.69×10⁷ SHU and a mean of 1.49×10⁷ SHU. These results indicated that although GCC of the agro-pastoral transition zone of northern China showed an overall increasing trend from 2000 to 2022, the forage-livestock balance index remained basically stable. The GCC was predicted to show a decreasing trend in the future. The findings provide a scientific basis for the sustainable development of grassland and the optimization of grazing management policies in this area.

Key words： grassland carrying capacity climate change forage-livestock balance grassland ecosystem grazing management

Received: 06 November 2024 Published: 30 June 2025

Corresponding Authors: ^*AI Zemin (E-mail: aizmxs@yeah.net)

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	LIU Huan
	YAO Yuyan
	AI Zemin
	DANG Xiaohu
	CAO Yong
	LI Qingqing
	HOU Mengjia
	HU Haoli
	ZHANG Yuanyuan
	CAO Tian

Cite this article:

LIU Huan, YAO Yuyan, AI Zemin, DANG Xiaohu, CAO Yong, LI Qingqing, HOU Mengjia, HU Haoli, ZHANG Yuanyuan, CAO Tian. Spatial and temporal evolution of forage-livestock balance in the agro-pastoral transition zone of northern China. Journal of Arid Land, 2025, 17(6): 754-771.

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http://jal.xjegi.com/10.1007/s40333-025-0016-8 OR http://jal.xjegi.com/Y2025/V17/I6/754

Table 1 Remote sensing data source

Table 2 Coupled Model Intercomparison Project Phase 6 (CMIP6) model data parameters from 2024 to 2030

Table 3 Ratio coefficients of belowground to aboveground biomass for different grassland types

Fig. 1 Annual variation of grassland carrying capacity (GCC) in the agro-pastoral transition zone of northern China. SHU, standard sheep unit.

Fig. 2 Spatial distribution of GCC in the agro-pastoral transition zone of northern China from 2000 to 2022. Note that the map is derived from the Geographical Information Monitoring Platform (http://www.dsac.cn) and the boundaries of provinces and autonomous regions are not revised.

Fig. 3 Geographical detector of driving factors in GCC from 2000 to 2022. DEM, digital elevation model; GDP, gross domestic product. PET, potential evapotranspiration.

Fig. 4 Spatial heterogeneity of driving factors in GCC in the agro-pastoral transition zone of northern China from 2000 to 2022. (a), cumulative solar radiation; (b), precipitation; (c), relative humidity.

Fig. 5 Distribution of forage-livestock balance index in the agro-pastoral transition zone of northern China from 2000 to 2020. Reasonable GCC indicates the total of theoretical GCC and supplementary feeding amount.

Fig. 6 Distribution of forage-livestock balance map of the agro-pastoral transition zone of northern China from 2000 to 2022.

Fig. 7 Taylor diagram of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model (a) and time variation of GCC in the agro-pastoral transition zone of northern China from 2000 to 2030 (b). The red dashed line in Figure 7a indicates the root mean square error, the black solid line indicates the numerical curve of the simulated standard deviation of the model, and the black dot is the corresponding value of the curve. The same color coverage area in Figure 7b means the standard error of the mean. RMSD, root mean square deviation.

Fig. 8 Spatial distribution of potential GCC under shared socioeconomic pathway (SSP)2-4.5 (a-g) and SSP5-8.5 (h-n) scenarios from 2024 (SSP2-4.5-2024 or SSP5-8.5-2024) to 2030 (SSP2-4.5-2030 or SSP5-8.5-2030)


[1]	Ba W R, Qiu H T, Cao Y G, et al. 2023. Spatiotemporal characteristics prediction and driving factors analysis of NPP in Shanxi Province covering the period 2001-2020. Sustainability, 15(15): 12070, doi: 10.3390/su151512070.

[2]	Bai S T, Yang J C, Zhang Y B, et al. 2022. Evaluating ecosystem services and trade-offs based on land-use simulation: A case study in the farming-pastoral ecotone of northern China. Land, 11(7): 1115, doi: 10.3390/land11071115.

[3]	Brunsdon C, Fotheringham A S, Charlton M E. 1996. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4): 281-298.

[4]	Chai Y F, Hu Y. 2024. Characteristics and drivers of vegetation productivity sensitivity to increasing CO₂ at northern middle and high latitudes. Ecology Evolution, 14(5): e11467, doi: 10.1002/ece3.11467.

[5]	Chen N, Jayaprakash C, Yu K L, et al. 2018. Rising variability, not slowing down, as a leading indicator of a stochastically driven abrupt transition in a dryland ecosystem. The American Naturalist, 191(1): E1-E14, doi: 10.1086/694821.

[6]	Chen Q G. 2005. Key pasture, seasonal grazing and sustainable development of grassland animal husbandry production in China. Acta Prataculturae Sinica, 14(4): 29-34. (in Chinese)

[7]	Chen Q G. 2008. Current status and development of grassland monitoring in China. Pratacultural Science, 25(2): 29-38. (in Chinese)

[8]	Chen S R, Wang S X, Zhou Y. 2008. Estimation of Chinese grassland productivity using remote sensing. Transactions of the CSAE, 24(1): 208-212. (in Chinese)

[9]	Chen T, Zheng H, Chen J, et al. 2024. Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring. Computers and Electronics in Agriculture, 219: 108807, doi: 10.1016/j.compag.2024.108807.

[10]	Chen Z F, Wang W G, Fu J Y. 2020. Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China. Scientific Reports, 10: 830, doi: 10.1038/s41598-020-57910-1. pmid: 31965046

[11]	Cheng C G, Zhou W, Chen Y Z, et al. 2014. Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environmental Earth Sciences, 72: 4273-4282.

[12]	Dai E F, Wang Y H. 2020. Spatial heterogeneity and driving mechanisms of water yield service in the Hengduan Mountain region. Acta Geographica Sinica, 75(3): 607-619. (in Chinese) doi: 10.11821/dlxb202003012

[13]	Dai L W, Tang H P, Pan Y L, et al. 2024. Assessment of ecosystem stability in the agro-pastoral transitional zone for local sustainable management: A case study in Duolun County, northern China. Ecological Indicators, 162: 112018, doi: 10.1016/j.ecolind.2024.112018.

[14]	De Leeuw J, Rizayeva A, Namazov E, et al. 2019. Application of the MODIS MOD 17 net primary production product in grassland carrying capacity assessment. International Journal of Applied Earth Observation and Geoinformation, 78: 66-76.

[15]	Dong S K, Jiang Y, Huang X X. 2002. Suitability degree of grassland grazing and strategies for pasture management. Resources Science, 24(6): 35-41. (in Chinese)

[16]	Du Q Q, Sun Y F, Guan Q Y, et al. 2022. Vulnerability of grassland ecosystems to climate change in the Qilian Mountains, Northwest China. Journal of Hydrology, 612: 128305, doi: 10.1016/j.jhydrol.2022.128305.

[17]	Du Q Q, Sun Y F, Guan Q Y, et al. 2024. Theoretical carrying capacity of grasslands and early warning for maintaining forage-livestock balance in the Qilian Mountains, Northwest China. Plant and Soil, 498: 225-241.

[18]	Dusenge M E, Duarte A G, Way D A. 2019. Plant carbon metabolism and climate change: Elevated CO₂ and temperature impacts on photosynthesis, photorespiration and respiration. New Phytologist, 221(1): 32-49.

[19]	Erb K-H, Kastner T, Plutzar C, et al. 2018. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553: 73-76.

[20]	Erdaw M M. 2023. Contribution, prospects and trends of livestock production in sub-Saharan Africa: A review. International Journal of Agricultural Sustainability, 21(1): 2247776, doi: 10.1080/14735903.2023.2247776.

[21]	Fenetahun Y, Yuan Y, Xu X W, et al. 2022. Borana rangeland of southern Ethiopia: Estimating biomass production and carrying capacity using field and remote sensing data. Plant Diversity, 44(6): 598-606.

[22]	Fuchslueger L, Bahn M, Hasibeder R, et al. 2016. Drought history affects grassland plant and microbial carbon turnover during and after a subsequent drought event. Journal of Ecology, 104(5): 1453-1465. pmid: 27609992

[23]	Guevara J, Cavagnaro J, Estevez O, et al. 1997. Productivity, management and development problems in the arid rangelands of the central Mendoza plains (Argentina). Journal of Arid Environments, 35(4): 575-600.

[24]	Guo Y, Jia Z B, Zhang Q, et al. 2021. Study on the spatiotemporal dynamics of forage-livestock balance in Hulunbuir Grassland of Inner Mongolia based on remote sensing data. Chinese Journal of Grassland, 43(4): 30-37. (in Chinese)

[25]	Guo Y J, Chen Y Y. 2024. A review of the impact of grazing on grassland ecosystems: Research progress and prospects. Advances in Resources Research, 4(3): 455-473.

[26]	Guo Z C, Li Y Q, Wang X Y, et al. 2023. Remote sensing of soil organic carbon at regional scale based on deep learning: A case study of agro-pastoral ecotone in northern China. Remote Sensing, 15(5): 3846, doi: 10.3390/rs15153846.

[27]	Hahn C, Lüscher A, Ernst-Hasler S, et al. 2021. Timing of drought in the growing season and strong legacy effects determine the annual productivity of temperate grasses in a changing climate. Biogeosciences, 18(2): 585-604.

[28]	He S Y, Xiong K N, Song S Z, et al. 2023. Research progress of grassland ecosystem structure and stability and inspiration for improving its service capacity in the Karst desertification control. Plants, 12(4): 770, doi: 10.3390/plants12040770.

[29]	Huang L, Ning J, Zhu P, et al. 2021. The conservation patterns of grassland ecosystem in response to the forage-livestock balance in North China. Journal of Geographical Sciences, 31: 518-534. doi: 10.1007/s11442-021-1856-6

[30]	Huang L, Wang D J, Yao L H, et al. 2019. Primary limitation on vegetation productivity shifts from precipitation in dry years to nitrogen in wet years in a degraded arid steppe of Inner Mongolia, northern China. Journal of Soils and Sediments, 19: 544-556. doi: 10.1007/s11368-018-2070-8

[31]	Jiang H L, Xu X, Guan M X, et al. 2020. Determining the contributions of climate change and human activities to vegetation dynamics in agro-pastoral transitional zone of northern China from 2000 to 2015. Science of the Total Environment, 718: 134871, doi: 10.1016/j.scitotenv.2019.134871.

[32]	Jin X M, Han G D. 2010. Evaluation of rangeland condition and estimation of grazing capacity in Stipa baicalensis steppe. Journal of Northeast Normal University, 42(1): 117-122. (in Chinese)

[33]	Jin Y X, Xu B, Yang X C, et al. 2011. Remote sensing dynamic estimation of grass production in Xilinguole, Inner Mongolia. Scientia Sinica, 41(12): 1185-1195. (in Chinese)

[34]	Jordon M W, Buffet J C, Dungait J A, et al. 2024. A restatement of the natural science evidence base concerning grassland management, grazing livestock and soil carbon storage. Proceedings of the Royal Society B, 291(2015): 20232669, doi: 10.1098/rspb.2023.2669.

[35]	Li C J, Fu B J, Wang S, et al. 2023. Climate-driven ecological thresholds in China's drylands modulated by grazing. Nature Sustainability, 6: 1363-1372.

[36]	Li M H, Wang J L, Li K, et al. 2024. Assessment of grazing livestock balance on the Eastern Mongolian Plateau based on remote sensing monitoring and grassland carrying capacity evaluation. Scientific Reports, 14: 32151, doi: 10.1038/s41598-024-84215-4.

[37]	Li S Y, Miao L J, Jiang Z H, et al. 2020. Projected drought conditions in northwest China with CMIP 6 models under combined SSPs and RCPs for 2015-2099. Advances in Climate Change Research, 11(3): 210-217.

[38]	Li T, Cui L Z, Scotton M, et al. 2022. Characteristics and trends of grassland degradation research. Journal of Soils and Sediments, 22: 1901-1912.

[39]	Li W J, Jiu C L, Tan Z H, et al. 2012. Natural grassland productivity and the livestock-feeds balance in Qinghai Province. Resources Science, 34(2): 367-372. (in Chinese)

[40]	Liu J H, Huang X, He X Y, et al. 2018. Estimation of grassland yield and carrying capacity in Qinghai Province based on MODIS data. Pratacultural Science, 35(10): 2520-2529. (in Chinese)

[41]	Liu L S, Zhang Y L, Bai W Q, et al. 2006. Characteristics of grassland degradation and driving forces in the source region of the Yellow River from 1985 to 2000. Journal of Geographical Sciences, 16: 131-142.

[42]	Liu Y H, Shi L, Chang H, et al. 2021. Analysis of driving factors that influence the pattern and quality of the ecosystem in Xilingol League. Acta Prataculturae Sinica, 30(12): 17-26. (in Chinese)

[43]	Ma B X, Jing J L, Liu B, et al. 2023. Assessing the contribution of human activities and climate change to the dynamics of NPP in ecologically fragile regions. Global Ecology and Conservation, 42: e02393, doi: 10.1016/j.gecco.2023.e02393.

[44]	Mai X H, Zhang Y J, Zhang Y J, et al. 2013. Research progress of grassland feed-animal balance at home and abroad. Asian Agricultural Research, 5(12): 73-76.

[45]	Petermann J S, Buzhdygan O Y. 2021. Grassland biodiversity. Current Biology, 31(19): R1195-R1201, doi: 10.1016/j.cub.2021.06.060.

[46]	Qi X L, Xu H J, Teng R Y, et al. 2024. Ecological restoration largely alleviates livestock grazing pressure in a montane grassland. Journal of Cleaner Production, 467: 143044, doi: 10.1016/j.jclepro.2024.143044.

[47]	Qian S, Wang L Y, Gong X F. 2012. Climate change and its effects on grassland productivity and carrying capacity of livestock in the main grasslands of China. The Rangeland Journal, 34(4): 341-347.

[48]	Qu Y B, Zhao Y Y, Ding G D, et al. 2021. Spatiotemporal patterns of the forage-livestock balance in the Xilin Gol Steppe, China: Implications for sustainably utilizing grassland-ecosystem services. Journal of Arid Land, 13(2): 135-151. doi: 10.1007/s40333-021-0053-x

[49]	Romero-Ruiz A, Monaghan R, Milne A, et al. 2023. Modelling changes in soil structure caused by livestock treading. Geoderma, 431: 116331, doi: 10.1016/j.geoderma.2023.116331.

[50]	Sedgwick P. 2012. Pearson's correlation coefficient. British Medical Journal, 345: e4483, doi: 10.1136/bmj.e4483.

[51]	Shi Y, Brierley G, Perry G L, et al. 2024. An improved approach to estimate stocking rate and carrying capacity based on remotely sensed phenology timing. Remote Sensing, 16(11): 1991, doi: 10.3390/rs16111991.

[52]	Song X Y, Xing Q M, Chang S J, et al. 2018. Comprehensive evaluation on bearing capacity of desert steppe in Inner Mongolia. Journal of Anhui Agricultural Sciences, 46(14): 93-96. (in Chinese)

[53]	Su D X, Meng Y D, Wu B G. 2002. Calculation of Reasonable Livestock Carrying Capacity of Natural Grassland in NY/T635-2002. Beijing: China Agriculture Press. (in Chinese)

[54]	Su R N, Zu J X, Jin H, et al. 2017. Changes in grassland productivity and livestock carrying capacity in Inner Mongolia. Ecology Environmental Sciences, 26(4): 605-612. (in Chinese)

[55]	Umuhoza J, Jiapaer G, Yin H M, et al. 2021. The analysis of grassland carrying capacity and its impact factors in typical mountain areas in Central AsiaA case of Kyrgyzstan and Tajikistan. Ecological Indicators, 131: 108129, doi: 10.1016/j.ecolind.2021.108129.

[56]	Wang G Q, Liu X Y. 2017. Discussion on the balance of grassland and livestock in Inner Mongolia. Ecological Economy, 33(4): 160-164. (in Chinese)

[57]	Wang J F, Xu C D. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116-134. (in Chinese) doi: 10.11821/dlxb201701010

[58]	Wang Q X, Okadera T, Nakayama T, et al. 2024a. Estimation of the carrying capacity and relative stocking density of Mongolian grasslands under various adaptation scenarios. Science of the Total Environment, 913: 169772, doi: 10.1016/j.scitotenv.2023.169772.

[59]	Wang Z Y, Zhou Y F, Sun X Y, et al. 2024b. Estimation of NPP in Huangshan district based on deep learning and CASA model. Forests, 15(8): 1467, doi: 10.3390/f15081467.

[60]	Wei W R, Zhang Y, Tang Z M, et al. 2022. Suitable grazing during the regrowth period promotes plant diversity in winter pastures in the Qinghai-Tibetan Plateau. Frontiers in Ecology Evolution, 10: 991967, doi: 10.3389/fevo.2022.991967.

[61]	Wen Y Y, Liu X P, Xin Q C, et al. 2019. Cumulative effects of climatic factors on terrestrial vegetation growth. Journal of Geophysical Research: Biogeosciences, 124(4): 789-806. doi: 10.1029/2018JG004751

[62]	Xiang Y Y, Wang Y, Chen Y N, et al. 2021. Impact of climate change on the hydrological regime of the Yarkant River Basin, China: An assessment using three SSP scenarios of CMIP6 GCMs. Remote Sensing, 14(1): 115, doi: 10.3390/rs14010115.

[63]	Xiong F Q, Zhang J L, Guo J, et al. 2025. Temporal and spatial changes in grass-animal balance in Wensu County, 2000-2022. Pratacultural Science, 42(1): 247-259. (in Chinese)

[64]	Xu B, Yang X C. 2009. Calculation of grass production and balance of livestock carrying capacity in rangeland region of Northeast China. Geographical Research, 28(2): 402-408. (in Chinese)

[65]	Xu B, Yang X C, Jin Y X, et al. 2012. Monitoring and evaluation of grassland-livestock balance in pastoral and semi-pastoral counties of China. Geographical Research, 31(11): 1998-2006. (in Chinese)

[66]	Xu H J, Zhao C Y, Wang X P. 2020. Elevational differences in the net primary productivity response to climate constraints in a dryland mountain ecosystem of northwestern China. Land Degradation & Development, 31(15): 2087-2103.

[67]	Xu X F. 2024. Frequent occurrence of extreme weather and out-of-balance climate systems. The Innovation Geoscience, 2(1): 100049, doi: 10.59717/j.xinn-geo.2024.100049.

[68]	Yan L Y, Kong L Q, Wang L J, et al. 2024. Grass-livestock balance under the joint influences of climate change, human activities and ecological protection on Tibetan Plateau. Ecological Indicators, 162: 112040, doi: 10.1016/j.ecolind.2024.112040.

[69]	Yan N N, Zhu W W, Wu B F, et al. 2023. Assessment of the grassland carrying capacity for winter-spring period in Mongolia. Ecological Indicators, 146: 109868, doi: 10.1016/j.ecolind.2023.109868.

[70]	Yang H L, Yao B, Su Y Z, et al. 2024. Distribution pattern of soil physical and chemical properties of plantation forest in northern agropastoral ecotone. Journal of Desert Research, 44(2): 283-294. (in Chinese)

[71]	Yang Y J, Wang K, Liu D, et al. 2020. Effects of land-use conversions on the ecosystem services in the agro-pastoral ecotone of northern China. Journal of Cleaner Production, 249: 119360, doi: 10.1016/j.jclepro.2019.119360.

[72]	Yang Z L, Yang G H. 2000. Potential productivity and livestock carrying capacity of high-frigid grassland in China. Resources Science, 22(4): 72-77. (in Chinese)

[73]	Yu C L, Liu D, Zhao H Y. 2021. Evaluation of the carbon sequestration of Zhalong Wetland under climate change. Journal of Geographical Sciences, 31: 938-964. doi: 10.1007/s11442-021-1879-z

[74]	Zeng Z G, Bi J H, Li S R, et al. 2014. Effects of habitat alteration on lizard community and food web structure in a desert steppe ecosystem. Biological Conservation, 179: 86-92.

[75]	Zhang G G, Kang Y M, Han G D, et al. 2011. Effect of climate change over the past half century on the distribution, extent and NPP of ecosystems of Inner Mongolia. Global Change Biology, 17(1): 377-389.

[76]	Zhang W H, Jia Z B, Zhuo Y, et al. 2016. Space dynamic change of pasture amount and influence factors analysis in Xilin Gol Grassland. Journal of Earth Environment, 7(2): 163-172. (in Chinese)

[77]	Zhang X Z, Li M, Wu J S, et al. 2022. Alpine grassland aboveground biomass and theoretical livestock carrying capacity on the Tibetan Plateau. Journal of Resources Ecology, 13(1): 129-141.

[78]	Zhang Z, Hu B Q, Jiang W G, et al. 2023. Spatial and temporal variation and prediction of ecological carrying capacity based on machine learning and PLUS model. Ecological Indicators, 154: 110611, doi: 10.1016/j.ecolind.2023.110611.

[79]	Zhou G S, Zheng Y R, Chen S Q. 1998. NPP model of natural vegetation and its application in China. Scientia Silvae Sinicae, 34(5): 2-11. (in Chinese)

[80]	Zhu P, Huang L, Zhai J, et al. 2022. Changing trends in grazing pressure in key ecological function area of the agro-pastoral zone. Pratacultural Science, 39(6): 1269-1279. (in Chinese)

[81]	Zhu Q, Chen H, Peng C H, et al. 2023. An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau. Nature Communications, 14: 6406, doi: 10.1038/s41467-023-42099-4. pmid: 37827999

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