Application of the RothC Model in Simulating Effect of Climate Change on CO2 Emissions and Soil Organic Carbon Stocks in Semi-arid Climate of Khorasan-e-Razavi

آب و خاک

Article 6, Volume 36, Issue 5 - Serial Number 85, November and December 2022, Pages 611-628   

Document Type: Research Article

DOI: 10.22067/jsw.2022.78752.1202

Authors

Saba Bagherifam¹; Mohammad Amir Delavar^{* 2}; Payman Keshavarz³; Parviz Karami⁴

¹PhD student, Dept. of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

²Associate Professor, Dept. of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.

³Associate Professor, Dept. of Soil and Water Research, Khorasan Razavi Agricultural and Natural Resources Research Center, AREEO. Mashhad, Iran.

⁴. Assistant Professor, Dept. of Range and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.

Abstract

Introduction
 Soil is one of the main drivers of global warming through losing carbon in the form of CO₂. On the other hand, its ability to sequester carbon is a suitable option for reducing CO₂ emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO₂ content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO₂ emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.
Materials and Methods
 The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO₂ cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.
Results and Discussion
 The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R², Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO₂ emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO₂ emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO₂ trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.
Conclusion
 The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO₂ emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO₂ emissions.

Keywords

Global warming; Carbon sequestration; Decomposition of soil organic matter; Model efficiency; Rothamsted carbon model

References

1. Alvaro-Fuentes, J., Lopez, M.V., Arrue, J.L., Moret, D., & Paustian, K. (2009). Tillage and cropping effects on soil organic carbon in Mediterranean semiarid agroecosystems: testing the Century model. Agriculture, Ecosystems and Environment 134(3-4): 211-217. https://doi.org/10.1016/j.agee.2009.07.001.
2. Anderson, T.H. (2003). Microbial eco-physiological indicators to asses soil quality. Agriculture, Ecosystems and Environment 98: 285-293. https://doi.org/10.1016/S0167-8809(03)00088-4.
3. Azad, B., & Afzali, S.F. (2019). Evaluation of two soil carbon models performance using measured data in semi-arid rangelands of Bajgah, Fars province. Iranian Journal of Soil and Water Research 50: 819-835. (In Persian with English abstract). 10.22059/ijswr.2018.264873.668001.
4. Babaeian, I., & Kouhi, M. (2012). Agroclimatic indices assessment over some selected weather stations of Khorasan Razavi province under climate change scenarios. Journal of Water and Soil 26: 953-967. (In Persian with English abstract).
5. Barančíková, G., Halas, J., Guttekova, M., Makovnikova, J., Novakova, M., Skalský, R., & Tarasovičová, Z. (2010). Application of RothC model to predict soil organic carbon stock on agricultural soils of Slovakia. Soil and Water Research 5: 1-9.
6. Batjes, N.H. (1996). Total carbon and nitrogen in the soils of the world. European Journal of Soil Science 47: 151-163. https://doi.org/10.1111/j.1365-2389.1996.tb01386.x.
7. Baveye, P.C., Schnee, L.S., Boivin, P., Laba, M., & Radulovich, R. (2020). Soil organic matter research and climate change: merely re-storing carbon versus restoring soil functions. Frontiers in Environmental Science 8: 579904. https://doi.org/10.3389/fenvs.2020.579904.
8. Bleuler, M., Farina, R., Francaviglia, R., di Bene, C., Napoli, R., & Marchetti, A. (2017). Modelling the impacts of different carbon sources on the soil organic carbon stock and CO₂ emissions in the Foggia province (Southern Italy). Agricultural Systems 157: 258-268. https://doi.org/10.1016/j.agsy.2017.07.017.
9. Bolinder, M., Janzen, H., Gregorich, E., Angers, D., & VandenBygaart, A. (2007). An approach for estimating net primary productivity and annual carbon inputs to soil for common agricultural crops in Canada. Agriculture, Ecosystems and Environment 118: 29-42. https://doi.org/10.1016/j.agee.2006.05.013.
10. Bond-Lamberty, B., & Thomson, A. (2010). Temperature-associated increases in the global soil respiration record. Nature 464: 579-582. https://doi.org/10.1016/j.agee.2006.05.013.
11. Chicco, D., Warrens, M.J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science 7: e623. https://doi.org/10.7717/peerj-cs.623.
12. Borrelli, L., Colecchia,S., Troccoli, A., Caradonna, S., Papini, R., Ventrella, D., Dell Abate, M, T., & Farina, R. (2011). Effectiveness of the GAEC standard of cross compliance crop rotations in maintaining organic matter levels in soil. Italian Journal of Agronomy 6: 57-62. https://doi.org/10.4081/IJA.2011.6.S1.E8.
13. Coleman, K., & Jenkinson, D. (1996). RothC-26.3-A Model for the turnover of carbon in soil. In "Evaluation of soil organic matter models", pp. 237-246. Springer. https://doi.org/10.1007/978-3-642-61094-3_17.
14. Coleman, K., & Jenkinson, D. (2014). RothC-A Model for the Turnover of Carbon in Soil-Model description and users guide. Rothamsted Research, Harpenden, UK.
15. Coleman, K., Jenkinson, D., Crocker, G., Grace, P., Klir, J., Körschens, M., Poulton, P., & Richter, D. (1997). Simulating trends in soil organic carbon in long-term experiments using RothC-26.3. Geoderma 81: 29-44. https://doi.org/10.1016/S0016-7061(97)00079-7.
16. Davidson, E.A., & Janssens, I.A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440: 165-173. http://doi.org/10.1038/nature04514.
17. Diele, F., Marangi, C., & Martiradonna, A. (2021). Non-standard discrete RothC Models for doil carbon dynamics. Axioms 10: 56. https://doi.org/10.3390/axioms10020056.
18. Dondini, M., Hastings, A., Saiz, G., Jones, M.B., & Smith, P. (2009). The potential of Miscanthus to sequester carbon in soils: comparing field measurements in Carlow, Ireland to model predictions. Gcb Bioenergy 1: 413-425. https://doi.org/10.1111/j.1757-1707. 2010.01033.x.
19. 19. Doner, H., & Lynn, W.C. (1989). Carbonate, halide, sulfate and sulfide minerals. P 279-330. In: Dixon, J. B and Weed, S. B (ed.) Minerals in environments. Second ed. Soil Science Society of Am. Madison, Wis. USA. https://doi.org/10.2136/sssabookser1.2ed.c6.
20. Eswaran, H. (2000). Global carbon stock. Global climate change and pedogenic carbonates, 15-25.
21. Fallahi, J., Rezvani, M.P., Nassiri, M.M., & Behdani, M. (2013). Validation of RothC model for evaluation of carbon sequestration in a restorated ecosystem under two different climatic scenarios. Journal of Water and Soil 3: 656-668. (In Persian with English abstract)
22. Falloon, P., Smith, P., Coleman, K., & Marshall, S. (1998). Estimating the size of the inert organic matter pool from total soil organic carbon content for use in the Rothamsted carbon model. Soil Biology and Biochemistry 30: 1207-1211. http://doi.10.1016/S0038-0717(97)00256-3.
23. Farina, R., Coleman, K., & Whitmore, A.P. (2013). Modification of the RothC model for simulations of soil organic C dynamics in dryland regions. Geoderma 200: 18-30. https://doi.org/10.1016/j.geoderma.2013.01.021.
24. Farina, R., Marchetti, A., Francaviglia, R., Napoli, R., & Di Bene, C. (2017). Modeling regional soil C stocks and CO₂ emissions under Mediterranean cropping systems and soil types. Agriculture, Ecosystems and Environment 238: 128-141. https://doi.org/10.1016/j.agee.2016.08.015.
25. Francaviglia, R., Baffi, C., Nassisi, A.L., Cassinari, C., & Farina, R. (2013).Use of the RothC model to simulate soil organic dynamic on a silty loam Inceptisol in northern Italy under different fertilization practices. Environmental Quality 11: 17-28. https://dx.doi.org/10.6092/issn.2281-4485/4085.
26. Francaviglia, R., Coleman, K., Whitmore, A.P., Doro, L., Urracci, G., Rubino, M., & Ledda, L. (2012). Changes in soil organic carbon and climate change–Application of the RothC model in agro-silvo-pastoral Mediterranean systems. Agricultural Systems 112: 48-54. https://doi.org/10.1016/j.agsy.2012.07.001.
27. Francaviglia, R., Renzi, G., Ledda, L., & Benedetti, A. (2017). Organic carbon pools and soil biological fertility are affected by land use intensity in Mediterranean ecosystems of Sardinia, Italy. Science of the Total Environment 599: 789-796. https://doi.org/10.1016/j.scitotenv.2017.05.021.
28. González-Molina, L., Etchevers-Barra, J., Paz-Pellat, F., Diaz-Solis, H., Fuentes-Ponce, M., Covaleda-Ocon, S., & Pando-Moreno, M. (2011). Performance of the RothC-26.3 model in short-term experiments in Mexican sites and systems. Agricultural Science 149: 415-425. https://doi.org/10.1017/S0021859611000232.
29. 2 Gorissen, A., Tietema, A., Joosten, N.N., Estiarte, M., Penuelas, J., Sowerby, A., Emmett, B.A., & Beier, C. (2004). Climate change affects carbon allocation to the soil in shrublands. Ecosystems 7: 650-661. https://doi.org/10.1007/s10021-004-0218-4.
30. Guo, Y., Wang, X., Li, X., Wang, J., Xu, M., & Li, D. (2016). Dynamics of soil organic and inorganic carbon in the cropland of upper Yellow River Delta, China. Scientific Reports 6: 1-10. https://doi.org/10.1038/srep36105.
31. Inubushi, K., Cheng, W., Mizuno, T., Lou, Y., Hasegawa, T., Sakai, H., & Kobayashi, K. (2011). Microbial biomass carbon and methane oxidation influenced by rice cultivars and elevated CO₂ in a Japanese paddy soil. European Journal of Soil Science 62: 69-73. https://doi.org/10.1111/j.1365-2389.2010.01323.x.
32. IPCC. (2021). "Climate Change Impacts, the Physical Science Basis."
33. 3 Jenkinson, D.S., Harris, H.C., Ryan, J., McNeill, A.M., Pilbeam, C.J., & Colman, K. (1999). Organic matter turnover in a calcareous clay soil from Syria under a two-course cereal rotation. Soil Biology and Biochemistry 31(5): 687-693. https://doi.org/10.1016/S0038-0717(98)00157-6.
34. Jordon, M.W., & Smith, P. (2022). Modelling soil carbon stocks following reduced tillage intensity: A framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe. Soil and Tillage Research 222: 105428. https://doi.org/10.1016/j.still.2022.105428.
35. Kaczynski, R., Siebielec, G., Hanegraaf, M.C., & Korevaar, H. (2017). Modelling soil carbon trends for agriculture development scenarios. Geoderma 286: 104-115. https://doi.org/10.1016/j.geoderma.2016.10.026.
36. Kaonga, M., & Coleman, K. (2008). Modelling soil organic carbon turnover in improved fallows in eastern Zambia using the RothC-26.3 model. Forest Ecology and Management 256: 1160-1166. https://doi.org/10.1016/j.foreco.2008.06.017.
37. Kirschbaum, M.U. (1995). The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biology and Biochemistry 27: 753-760. https://doi.org/10.1016/0038-0717(94)00242-S.
38. 3 Klute, A. (1986). Water retention: laboratory methods. Methods of soil analysis: Part 1 Physical and mineralogical methods 632-662. https://doi.org/10.2136/sssabookser5.1.2ed.c26.
39. Kolosz, B.W., Sohi, S.P., & Manning, D. (2019). CASPER: A modelling framework to link mineral carbonation with the turnover of organic matter in soil. Computers and Geosciences 124: 58-72. https://doi.org/10.1016/j.cageo.2018.12.012.
40. Lal, R. (2004). Soil carbon sequestration impacts on global climate change and food security. Science 304: 1623-1627. https://doi.org/10.1126/science.1097396.
41. Lal, R. (2013). Soil carbon management and climate change. Carbon Management 4: 439-462. https://doi.org/10.4155/cmt.13.31.
42. Lefèvre, C., Rekik, F., Alcantara, V., & Wiese, L. (2017). "Soil organic carbon: the hidden potential," Food and Agriculture Organization of the United Nations (FAO).
43. Leifeld, J., Reiser, R., & Oberholzer, H. (2009). Consequences of conventional versus organic farming on soil carbon: results from a 27‐year field experiment. Agronomy 101: 1204-1218. https://doi.org/10.2134/agronj2009.0002.
44. Lopez-Bellido, R.J., Fontan, J.M., Lopez-Bellido, F.J., & Lopez- Bellido, L. (2010). Carbon sequestration by tillage, rotation and nitrogen fertilization in a Mediterranean Vertisol. Agronomy 102(1): 310-318. https://doi.org/10.2134/agronj2009.0165.
45. Luxmoore, R.J., Tharp, M.L., & Post, W.M. (2008). Simulated biomass and soil carbon of loblolly pine and cottonwood plantations across a thermal gradient in southeastern United States. Forest Ecology and Management 254: 291-299. https://doi.org/10.1016/j.foreco.2007.08.008.
46. Martins, C.S., Macdonald, C.A., Anderson, I.C., & Singh, B.K. (2016). Feedback responses of soil greenhouse gas emissions to climate change are modulated by soil characteristics in dry land ecosystems. Soil Biology and Biochemistry 100: 21-32. https://doi.org/10.1016/j.soilbio.2016.05.007.
47. 4 Mansouri, M. (2000). Reconnaissance survey and land classification of Jolge rokh, Torbat-e Heydariyeh. Technical report No 1089 (In Persian).
48. 4 Mondini, C., Cayuela, M.L., Sinicco, T., Fornasier, F., Galvez, A., & Sánchez-Monedero, M.A. (2017). Modification of the RothC model to simulate soil C mineralization of exogenous organic matter. Biogeosciences 14: 3274-3253. https://doi.org/10.5194/bg-14-3253-2017.
49. Mondini, C., Coleman, K., & Whitmore, A. (2012). Spatially explicit modelling of changes in soil organic C in agricultural soils in Italy, 2001–2100: Potential for compost amendment. Agriculture, Ecosystems and Environment 153: 24-32. https://doi.org/10.1016/j.agee.2012.02.020.
50. Muñoz-Rojas, M., Abd-Elmabod, S.K., Zavala, L.M., De la Rosa, D., & Jordán, A. (2017). Climate change impacts on soil organic carbon stocks of Mediterranean agricultural areas: a case study in Northern Egypt. Agriculture, Ecosystems and Environment 238: 142-158. https://doi.org/10.1016/j.agee.2016.09.001.
51. Navarro-Pedreño, J., Almendro-Candel, M.B., & Zorpas, A.A. (2021). The increase of soil organic matter reduces global warming, myth or reality? Science 3: 18. https://doi.org/10.3390/sci3010018.
52. Niklińska, M., Maryański, M., & Laskowski, R. (1999). Effect of temperature on humus respiration rate and nitrogen mineralization: Implications for global climate change. Biogeochemistry 44: 239-257. https://doi.org/10.1007/BF00996992.
53. Paul, K., Polglase, P., & Richards, G. (2003). Predicted change in soil carbon following afforestation or reforestation, and analysis of controlling factors by linking a C accounting model (CAMFor) to models of forest growth, litter decomposition and soil C turnover (RothC). Forest Ecology and Management 177: 485-501. https://doi.org/10.1016/S0378-1127(02)00454-1.
54. Paustian, K., Parton, W.J., & Persson, J. (1992). Modeling soil organic matter in organic‐amended and nitrogen‐fertilized long‐term plots. Soil Science Society of America Journal 56: 476-488. https://doi.org/10.2136/sssaj1992.03615995005600020023x.
55. Sakurai, G., Jomura, M., Yonemura, S., Iizumi, T., Shirato, Y., & Yokozawa, M. (2012). Inversely estimating temperature sensitivity of soil carbon decomposition by assimilating a turnover model and long-term field data. Soil Biology and Biochemistry 46: 191-199. https://doi.org/10.1016/j.soilbio.2011.11.005.
56. Schimel, D.S., House, J.I., Hibbard, K.A., Bousquet, P., Ciais, P., Peylin, P., Braswell, B.H., Apps, M.J., Baker, D., & Bondeau, A. (2001). Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414: 169-172. http://doi.org/10.1016/35102500.
57. Schindlbacher, A., Rodler, A., Kuffner, M., Kitzler, B., Sessitsch, A., & Zechmeister-Boltenstern, S. (2011). Experimental warming effects on the microbial community of a temperate mountain forest soil. Soil Biology and Biochemistry 43: 1417-1425. https://doi.org/10.1016/j.soilbio.2011.03.005.
58. Schmidt, M.W., Torn, M.S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., & Manning, D.A. (2011). Persistence of soil organic matter as an ecosystem property. Nature 478: 49-56. https://doi.org/10.1038/nature10386.
59. Shirato, Y., & Yokozawa, M. (2006). Acid hydrolysis to partition plant material into decomposable and resistant fractions for use in the Rothamsted carbon model. Soil Biology and Biochemistry 38: 812-816. https://doi.org/10.1016/j.soilbio.2005.07.008.
60. Shpedt, A., Ligaeva, N., & Emelyanov, D. (2019). Transformation of soil and land resources of the Middle Siberia in the conditions of climatic changes. In "IOP Conference Series: Earth and Environmental Science", Vol. 315, pp. 052051. IOP Publishing. doi:10.1088/1755-1315/315/5/052051.
61. Skjemstad, J., Spouncer, L., Cowie, B., & Swift, R. (2004). Calibration of the Rothamsted organic carbon turnover model (RothC ver. 26.3), using measurable soil organic carbon pools. Soil Research 42: 79-88. https://doi.org/10.1071/SR03013.
62. Smith, J., & Smith, P. (2007). "Environmental modelling: an introduction," Oxford University Press.
63. Smith, J., Smith, P., Wattenbach, M., Zaehle, S., Hiederer, R., Jones, R.J., Montanarella, L., Rounsevell, M.D., Reginster, I., & Ewert, F. (2005). Projected changes in mineral soil carbon of European croplands and grasslands, 1990–2080. Global Change Biology 11: 2141-2152. https://doi.org/10.1111/j.1365-2486.2005.001075.x.
64. Smith, P., Fang, C., Dawson, J.J., & Moncrieff, J.B. (2008). Impact of global warming on soil organic carbon. Advances in Agronomy 97: 1-43. https://doi.org/10.1016/S0065-2113(07)00001-6.
65. 6 Smith, P., Powlson, D., & Glendining, M. (1996). Establishing a European GCTE soil organic matter network (SOMNET). pp. 81-97. Springer. https://doi.org/10.1007/978-3-642-61094-3_7.
66. Smith, P., Smith, J., Franko, U., Kuka, K., Romanenkov, V., Shevtova, L., Wattenbach, M., Gottschalk, P., Sirotenko, O., & Rukhovich, D. (2007). Changes in mineral soil organic carbon stocks in the croplands of European Russia and the Ukraine, 1990–2070; comparison of three models and implications for climate mitigation. Regional Environmental Change 7: 105-119. https://doi.org/10.1007/s10113-007-0028-2.
67. Soil Survey Manual. (2014). Kellogg soil survey laboratory methods manual. Soil survey investigations report No 51, version 2 R Burt and soil survay staff (ed), U. S. Department of Agriculture Natural Resources conservation Service.
68. Soleimani, A., Hosseini, S.M., Bavani, A.R.M., Jafari, M., & Francaviglia, R. (2017). Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests. Science of the Total Environment 599: 1646-1657. https://doi.org/10.1016/j.scitotenv.2017.05.077.
69. Stockmann, U., Adams, M.A., Crawford, J.W., Field, D.J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A. B., De Courcelles, V.D.R., & Singh, K. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems and Environment 164: 80-99. https://doi.org/10.1016/j.agee.2012.10.001.
70. Thornthwaite, C.W. (1948). An approach toward a rational classification of climate. Geographical Review 38: 55-94. https://doi.org/10.2307/210739.
71. Vaghefi, S.A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H., & Abbaspour, K. (2019). The future of extreme climate in Iran. Scientific Reports 9: 1-11. https://doi.org/10.1038/s41598-018-38071-8.
72. Wan, Y., Lin, E., Xiong, W., & Guo, L. (2011). Modeling the impact of climate change on soil organic carbon stock in upland soils in the 21^st century in China. Agriculture, Ecosystems and Environment 141: 23-31. https://doi.org/10.1016/j.agee.2011.02.004.
73. Weihermüller, L., Graf, A., & Verecken, H. (2013). Simple pedotransfer functions to initialize reactive carbon pools of the RothC model. European Journal of Soil Science 64: 567-575. https://doi.org/10.1111/ejss.12036.
74. Xu, X., Liu, W., & Kiely, G. (2011). Modeling the change in soil organic carbon of grassland in response to climate change: Effects of measured versus modelled carbon pools for initializing the Rothamsted Carbon model. Agriculture, Ecosystems and Environment 140: 372-381. https://doi.org/10.1016/j.agee.2010.12.018.
75. Zimmermann, M., Leifeld, J., Schmidt, M., Smith, P., & Fuhrer, J. (2007). Measured soil organic matter fractions can be related to pools in the RothC model. European Journal of Soil Science 58: 658-667. https://doi.org/10.1111/j.1365-2389.2006.00855.x.