Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-1109-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-1109-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
19 Dec 2025
Original research article |
| 19 Dec 2025
| 19 Dec 2025
Soil organic carbon projections and climate adaptation strategies across Pacific Rim agro-ecosystems
Chien-Hui Syu
Agricultural Chemistry Division, Taiwan Agricultural Research Institute, Taichung City 40227, Taiwan, ROC
Chun-Chien Yen
Agricultural Chemistry Division, Taiwan Agricultural Research Institute, Taichung City 40227, Taiwan, ROC
Department of Soil and Environmental Sciences, National Chung Hsing University, Taichung 40227, Taiwan, ROC
Selly Maisyarah
Department of Soil and Environmental Sciences, National Chung Hsing University, Taichung 40227, Taiwan, ROC
Bo-Jiun Yang
Agricultural Chemistry Division, Taiwan Agricultural Research Institute, Taichung City 40227, Taiwan, ROC
Yu-Min Tzou
Department of Soil and Environmental Sciences, National Chung Hsing University, Taichung 40227, Taiwan, ROC
Shih-Hao Jien
CORRESPONDING AUTHOR
Department of Soil and Environmental Sciences, National Chung Hsing University, Taichung 40227, Taiwan, ROC
Cited articles
Adhikari, K., Hartemink, A. E., Minasny, B., Bou Kheir, R., Greven, M. B., and Greve, M. H.: Digital mapping of soil organic carbon contents and stocks in Denmark, PLoS ONE 9, e105519, https://doi.org/10.1371/journal.pone.0105519, 2014.
Blake, G. R. and Hartge, K. H.: Bulk density, in: Methods of soil analysis. Part 1. Physical and Mineralogical Methods, 2nd Edn., edited by: Klute, A., ASA, SSSA, Madison, WI, 363–375, https://doi.org/10.2136/sssabookser5.1.2ed.c13, 1986.
Breiman, L.: Random forests, Mach. Learni., 41, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Chalchissa, F. B. and Kuris, B. K.: Modeling the impacts of extreme climate scenarios on soil acidity (pH and exchangeable aluminum) in Abbay River Basin, Ethiopia, Heliyon, 10, 12, e33448, https://doi.org/10.1016/j.heliyon.2024.e33448, 2024.
Chalchissa, F. B., Diga, G. M., Feyisa, G. L., and Tolossa, A.R.: Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia, Heliyon, 8, e10136, https://doi.org/10.1016/j.heliyon.2022.e10136, 2022.
Chen, F., Wang, S. J., Dong, Q. J., Esper, J., Büntgen, U., Meko, D., Linderholm, H. W., Wang, T., Yue, W. P., Zhao, X. E., Hadad, M., González-Reyes, Á., and Chen, F. H.: Role of Pacific Ocean climate in regulating runoff in the source areas of water transfer projects on the Pacific Rim, npj Clim. Atmos. Sci., 7, 153, https://doi.org/10.1038/s41612-024-00706-1, 2024.
Dialynas, Y. G., Bastola, S., Bras, R. L., Billings, S. A., Markewitz, D., and Richter, D. D. B.: Topographic variability and the influence of soil erosion on the carbon cycle, Global Biogeochem. Cy., 30, 644–660, https://doi.org/10.1002/2015GB005302, 2016.
Edmondson, J. L., Davies, Z. G., McCormack, S. A., Gaston, K. J., and Leake, J. R.: Land–cover effects on soil organic carbon stocks in a European city, Sci. Total Environ., 472, 444–453, https://doi.org/10.1016/j.scitotenv.2013.11.025, 2014.
Elbasiouny, H., El-Ramady, H., Elbehiry, F., Rajput, V. D., Minkina, T., and Mandzhieva, S.: Plant Nutrition under Climate Change and Soil Carbon Sequestration, Sustainability, 14, 914, https://doi.org/10.3390/su14020914, 2022.
Freeman, E. A. and Moisen, G. G.: Evaluating kriging as a tool to improve moderate resolution maps of forest biomass, Environ. Monit. Assess., 128, 395–410, https://doi.org/10.1007/s10661-006-9322-6, 2007.
Grace, J.: Understanding and managing the global carbon cycle, J. Ecol., 92, 189–202, https://doi.org/10.1111/j.0022-0477.2004.00874.x, 2004.
Gray, J. M., Bishop, T. F. A., and Wilson, B. R.: Factors controlling soil organic carbon stocks with depth in eastern Australia, Soil Sci. Soc. Am. J., 79, 1741–1751, https://doi.org/10.2136/sssaj2015.06.0224, 2015.
Grunwald, S.: Multi-criteria characterization of recent digital soil mapping and modeling approaches, Geoderma, 152, 195–207, https://doi.org/10.1016/j.geoderma.2009.06.003, 2009.
Guo, L., Shi, T. Z., Linderman, M., Chen, Y. Y., Zhang, H. T., and Fu, P.: Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging, Remote Sens., 11, 1032, https://doi.org/10.3390/rs11091032, 2019.
Hijmans, R. J.: raster: Geographic data analysis and modeling, CRAN, https://doi.org/10.32614/CRAN.package.raster, 2022.
IPCC: 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use, in: Volume 4, https://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html (last access: 22 October 2025), 2006.
IPCC: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use, in: Volume 4, https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html (last access: 22 October 2025), 2019a.
IPCC: Climate Change and Land: Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, https://doi.org/10.1017/9781009157988, 2019b.
Jenny, H.: Factors of soil formation: a system of quantitative pedology, McGraw Hill, New York, New York, ISBN 0598537856, 1941.
Jien, S. H., Minasny, B., Yang, B. J., Liu, Y. T., Yen, C. C., Ocba, M. A., Zhang, Y. T., and Syu, C. H.: Enhancing Soil Carbon Storage: Developing high-resolution maps of topsoil organic carbon sequestration potential in Taiwan, Geoderma, 459, 117369, https://doi.org/10.1016/j.geoderma.2025.117369, 2025.
Johnston, C. A., Groffman, P., Breshears, D. D., Cardon, Z. G., Currie, W., Emanuel, W., Gaudinski, J., Jackson, R. B., Lajtha, K., Nadelhoffer, K., Neslon Jr., N., and Post, W. M.: Carbon cycling in soil, Front. Ecol. Environ., 2, 522–528, https://doi.org/10.1890/1540-9295(2004)002[0522:CCIS]2.0.CO;2, 2004.
Keskin, H. and Grunwald, S.: Regression kriging as a workhorse in the digital soil mapper's toolbox, Geoderma, 326, 22–41, https://doi.org/10.1016/j.geoderma.2018.04.004, 2018.
Khoshvaght, H., Permala, R. R., Razmjou, A., and Khiadani, M.: A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction, J. Envirom. Chem. Eng., 13, 119675, https://doi.org/10.1016/j.jece.2025.119675, 2025.
Kuhn, M.: Building Predictive Models in R Using the caret Package, J. Stat. Softw., 28, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008.
Kuhn, M. and Quinlan, R. Cubist: Rule- And Instance-Based Regression Modeling [code], CRAN, https://doi.org/10.32614/CRAN.package.Cubist (last access: 17 December 2025), 2022.
Lacoste, M., Minasny, B., McBratney, A., Michot, D., Viaud, V., and Walter, C.: High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape, Geoderma, 213, 296–311, https://doi.org/10.1016/j.geoderma.2013.07.002, 2014.
Lamichhane, S., Kumar, L., and Wilson, B.: Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review, Geoderma, 352, 395–413, https://doi.org/10.1016/j.geoderma.2019.05.031, 2019.
Li, H. W., Wu, Y. P., Chen, J., Zhao, F., Wang, F., Sun, Y. H., Zhang, G. C., and Qiu, L. J.: Responses of soil organic carbon to climate change in the Qilian Mountains and its future projection, J. Hydrol., 596, 126110, https://doi.org/10.1016/j.jhydrol.2021.126110, 2021.
Liaw, A. and Wiener, M.: Classification and regression by RandomForest, R News, 18–22, https://CRAN.R-project.org/doc/Rnews/ (last access: 17 December 2025), 2002.
Ma, Y. X., Minasny, B., and Wu, C.: Mapping key soil properties to support agricultural production in Eastern China, Geoderma Reg., 10, 144–153, https://doi.org/10.1016/j.geodrs.2017.06.002, 2017.
Malone, B. P., Minasny, B., POdgers, N., and McBratney, A. B.: Using model averaging to combine soil property rasters from legacy soil maps and from point data, Geoderma, 232, 34–44, https://doi.org/10.1016/j.geoderma.2014.04.033, 2014.
McBratney, A. B., Santos, M. M., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003.
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the area of applicability of spatial prediction models, Meth. Ecol. Evol., 12, 1620–1633, https://doi.org/10.1111/2041-210X.13650, 2021.
Minasny, B. and McBratney, A. B.: Digital soil mapping: A brief history and some lessons, Geoderma, 264, 301–311, https://doi.org/10.1016/j.geoderma.2015.07.017, 2016.
Mishra, U. and Riley, W. J.: Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks, Biogeosciences, 12, 3993–4004, https://doi.org/10.5194/bg-12-3993-2015, 2015.
Nelson, D. A. and Sommers, L.E.: Total carbon, organic carbon, and organic matter, in: Methods of soil analysis. Part 3, Chemical Methods, edited by: Sparks, D. L., ASA SSSA, Madison, WI, 961–1010, https://doi.org/10.2136/sssabookser5.3.c34, 1996.
Olaya-Abril, A., Parras-Alcántara, L., Lozano-García, B., and Obregón-Romero, R.: Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis, Sci. Total Environ., 592, 134–143, https://doi.org/10.1016/j.scitotenv.2017.03.021, 2017.
Peng, Y., Zhou, W., Xiao, J., Liu, H., Wang, T., and Wang, K.: Comparison of Soil Organic Carbon Prediction Accuracy Under Different Habitat Patches Division Methods on the Tibetan Plateau, Land Degrad. Dev., 1–14, https://doi.org/10.1002/ldr.70184, 2025.
Poeplau, C., Don, A., Vesterdal, L., Leifeld, J., Van Wesemael, B., Schumacher, J., and Gensior, A.: Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach, Global Change Biol., 17, 2415–2427, https://doi.org/10.1111/j.1365-2486.2011.02408.x, 2011.
Pouladi, N., Møller, A. B., Tabatabai, S., and Greve, M. H.: Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging, Geoderma, 342, 85–92, https://doi.org/10.1016/j.geoderma.2019.02.019, 2019.
Quinlan, J. R.: Learning with Continuous Classes, in: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, edited by: Adams, A. and Sterling, L., World Scientific, ISBN 981021250X, 343–348, 1992.
Rial, M., Cortizas, A. M., and Rodríguez-Lado, L.: Understanding the spatial distribution of factors controlling topsoil organic carbon content in European soils, Sci. Total Environ., 609, 1411–1422, https://doi.org/10.1016/j.scitotenv.2017.08.012, 2017.
Rillig, M. C., Leifheit, E., and Lehmann, J.: Microplastic effects on carbon cycling processes in soils, PLoS Biol., 19, e3001130, https://doi.org/10.1371/journal.pbio.3001130, 2021.
Rudiyanto, Minasny, B., Setiawan, B. I., Saptomo, S. K., and McBratney, A. B.: Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands, Geoderma, 313, 25–40, https://doi.org/10.1016/j.geoderma.2017.10.018, 2018.
Sanchez, G., Trinchera, L., and Russolillo, G.: plspm: tools for partial least squares path modeling (PLS-PM), R Package Version 4, CRAN, p. 9, https://CRAN.R-project.org/package=plspm (last access: 17 December 2025), 2017.
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., and Hair, J. F.: Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers, J. Fam. Bus. Strat., 5, 105–115, https://doi.org/10.1016/j.jfbs.2014.01.002, 2014.
Seitz, D., Dechow, R., Emde, D., Schneider, F., and Don, A.: Improved Broad-Scale Modelling of Soil Organic Carbon Dynamics Following Land-Use Changes, Eur. J. Soil Sci., 76, e70159, https://doi.org/10.1111/ejss.70159, 2025.
Shaik, A. B. and Srinivasan, S.: A Brief Survey on Random Forest Ensembles in Classification Model, in: International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, 56, edtied by: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., and Pan, I., Springer, Singapore, 253–260, https://doi.org/10.1007/978-981-13-2354-6_27, 2019.
Shi, J., Song, M., Yang, L., Zhao, F., Wu, J., Li, J., Yu, Z., Li, A., Shangguan, Z., and Deng, L.: Recalcitrant organic carbon plays a key role in soil carbon sequestration along a long-term vegetation succession on the Loess Plateau, Catena, 233, 107528, https://doi.org/10.1016/j.catena.2023.107528, 2023.
Siewert, M. B.: High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment, Biogeosciences, 15, 1663–1682, https://doi.org/10.5194/bg-15-1663-2018, 2018.
Singh, B. P., Setia, R., Wiesmeier, M., and Kunhikrishnan, A.: Chapter 7 – Agricultural management practices and soil organic carbon storage, Soil Carbon Storage, 2018, 207–244, https://doi.org/10.1016/B978-0-12-812766-7.00007-X, 2018.
Smith, P.: How long before a change in soil organic carbon can be detected?, Global Clim. Biol., 10, 1878–1883, https://doi.org/10.1111/j.1365-2486.2004.00854.x, 2004.
Therneau, T. and Atkinson, B.: Rpart: Recursive Partitioning and Regression Trees, R Package Version 4.1-15, CRAN, https://CRAN.R-project.org/package=rpart (last access: 3 November 2025), 2022.
Tsui, C. C., Liu, X. N., Guo, H. Y., and Chen, Z. S.: Effect of Sampling Density on Estimation of Regional Soil Organic Carbon Stock for Rural Soils in Taiwan, Geospatial Technology–Environmental and Social Applications, IntechOpen, 35–53, https://doi.org/10.5772/64210, 2016.
Vaysse, K. and Lagacherie, P.: Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France), Geoderma Reg., 4, 20–30, https://doi.org/10.1016/j.geodrs.2014.11.003, 2015.
Vereecken, H., Amelung, W., Bauke, S. L., Bogena, H., Brüggeman, N., Montzka, C., Vanderborght, J., Bechtold, M., Blöschl, G., Carminati, A., Javaux, M., Konings, A. G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne, S., Verhoef, A., Young, M., and Zhang, Y.: Soil hydrology in the Earth system, Nat. Rev. Earth Environ., 3, 573–587, https://doi.org/10.1038/s43017-022-00324-6, 2022.
Viaud, V., Angers, D. A., and Walter, C.: Toward landscape-scale modeling of soil organic matter dynamics in agroecosystems, Soil Sci. Soc. Am. J., 74, 1847–1860, https://doi.org/10.2136/sssaj2009.0412, 2010.
Villanueva, R. A. M. and Chen, Z. J.: ggplot2: Elegant Graphics for Data Analysis, in: 2nd Edn., Measurement – Interdisciplin. Res. Perspect., 17, 160–167, https://doi.org/10.1080/15366367.2019.1565254, 2019.
Viscarra Rossel, R. A., Zhang, M., Behrens, T., and Webster, R.: A warming climate will make Australian soil a net emitter of atmospheric CO2. npj Clim. Atmos. Sci., 7, 79, https://doi.org/10.1038/s41612-024-00619-z, 2024.
Wang, B., Waters, C., Orgill, S., Gary, J., Cowie, A., Clark, A., and Liu, D. L.: High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia, Sci. Total Environ., 630, 367–378, https://doi.org/10.1016/j.scitotenv.2018.02.204, 2018.
Wang, M. M., Zhang, S., Guo, X. W., Xiao, L. J., Yang, Y. H., Luo, Y. Q., Mishra, U., and Luo, Z. K.: Responses of soil organic carbon to climate extremes under warming across global biomes, Nat. Clim. Change, 14, 98–105, https://doi.org/10.1038/s41558-023-01874-3, 2023.
Wang, Z., Kumar, J., Weintraub-Leff, S. R., Todd-Brown, K., Mishra, U., and Sihi, D.: Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning, J. Geophys. Res.-Biogeo., 129, e2023JG007702, https://doi.org/10.1029/2023JG007702, 2024.
Wei, Y. C., Wang, M. M., Viscarra Rossel, R. A., Chen, H., and Luo, Z. K.: Extreme Climate as the Primary Control of Global Soil Organic Carbon Across Spatial Scales, Global Biogeochem. Cy., 38, e2024GB008277, https://doi.org/10.1029/2024GB008200, 2024.
Wickham, H.: ggplot2: Elegant graphics for data analysis, in: Springer International Publishing: Imprint: Use R!, 2nd Edn., Springer, Cham, https://doi.org/10.1007/978-3-319-24277-4, 2016.
Wiesmeier, M., Urbanski, L., Hobley, E., Lützow, M. V., Marin-Spiotta, E., Wesemael, B. V., Rabot, E., Ließ, M., Garcia-Franco, N., Wollschläger, U., Vogel, H. J., and Kögel-Knabner, I.: Soil organic carbon storage as a key function of soils – A review of drivers and indicators at various scales, Geoderma, 333, 149–162, https://doi.org/10.1016/j.geoderma.2018.07.026, 2019.
Zhang, G. Y., Luo, S., Jing, Z. W., Wei, S., and Ma, Y. H.: Evaluation and Forewarning Management of Regional Resources and Environment Carrying Capacity: A Case Study of Hefei City, Anhui Province, China, Sustainability, 12, 1637, https://doi.org/10.3390/su12041637, 2020.
Zhu, Q. and Lin, H. S.: Comparing ordinary kriging and regression kriging for Soil properties in contrasting landscapes, Pedosphere, 20, 594–606, https://doi.org/10.1016/S1002-0160(10)60049-5, 2010.
Short summary
The current global context, marked by this year's intense El Niño, underscores the growing severity and destructive consequences of climate change, particularly across the Pacific Rim. With a noticeable increase in the frequency and severity of climate-related disasters, there is a profound and pressing need for effective strategies to enhance regional resilience and mitigate future risks.
The current global context, marked by this year's intense El Niño, underscores the growing...
