Beyond clay: towards an improved set of variables for predicting soil organic matter content
- 2.3k Downloads
Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.
KeywordsSoil organic matter Biogeochemistry Carbon cycle
This work was conducted as a part of the “What Lies Below? Improving quantification and prediction of soil carbon storage, stability, and susceptibility to disturbance” Working Group supported by the John Wesley Powell Center for Analysis and Synthesis, funded by the U.S. Geological Survey. Additional support was provided by NSF EAR-1331408 and EAR- 1123454 to Rasmussen, NSF CAREER BCS-1349952 to Marin-Spiotta, US Department of Agriculture NIFA 2015-67003-23485 and US Department of Energy TES DE-SC0014374 to Wieder, and USDA-NIFA Hatch project HAW01130-H to Crow. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
This work is the result of two workshops sponsored by the USGS John Wesley Powell Center for Analysis and Synthesis in May of 2016 and May of 2017. The motivation and ideas for this work were generated collaboratively among all authors during these workshops. Rasmussen led manuscript development, data compilation, and analysis. All authors contributed to writing/editing, statistical analyses, and figure development.
- Buol SW, Eswaran H (2000) Oxisols. In: Sparks DL (ed) Advances in agronomy, vol 68. Academic Press, San Diego, pp 151–195Google Scholar
- Deng Y, Dixon JB (2002) Soil organic matter and organo-mineral interactions. In: Dixon JB, Schulze DG (eds) Soil Mineralogy with Environmental Applications. Soil Science Society of America, Madison, pp 69–108Google Scholar
- Doetterl S, Stevens A, Six J, Merckx R, Van Oost K, Pinto MC, Casanova-Katny A, Munoz C, Boudin M, Venegas EZ, Boeckx P (2015) Soil carbon storage controlled by interactions between geochemistry and climate. Nat Geosci 8(10): 780Google Scholar
- Douglas L (1989) Vermiculites. In: Dixon JB, Weed SB, Dinauer RC (eds) Minerals in soil environments. vol no 1. Soil Science Society of America, Madison, Wis., USA. p 635-674Google Scholar
- El Swaify SA (1980) Physical and mechanical properties of Oxisols. In: Theng BKG (ed) Soils with variable charge. New Zealand Society of Soil Science, Lower Hutt, pp 303–324Google Scholar
- Harsh J, Chorover J, Nizeyimana E (2002) Allophane and imogolite. In: Dixon JB, Schulze DG (eds) Soil Mineralogy with Environmental Applications. Soil Science Society of America, Madison, pp 291–322Google Scholar
- Hengl T, de Jesus JM, Heuvelink GBM, Gonzalez MR, Kilibarda M, Blagotic A, Shangguan W, Wright MN, Geng XY, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen B (2017) SoilGrids250 m: Global gridded soil information based on machine learning. Plos One 12(2)Google Scholar
- Ito A, Wagai R (2017) Global distribution of clay-size minerals on land surface for biogeochemical and climatological studies. Sci Data 4:Google Scholar
- Minasny B, Malone BP, McBratney AB, Angers DA, Arrouays D, Chambers A, Chaplot V, Chen ZS, Cheng K, Das BS, Field DJ, Gimona A, Hedley CB, Hong SY, Mandal B, Marchant BP, Martin M, McConkey BG, Mulder VL, O’Rourke S, Richer-de-Forges AC, Odeh I, Padarian J, Paustian K, Pan GX, Poggio L, Savin I, Stolbovoy V, Stockmann U, Sulaeman Y, Tsui CC, Vagen TG, van Wesemael B, Winowiecki L (2017) Soil carbon 4 per mille. Geoderma 292:59–86CrossRefGoogle Scholar
- Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’Amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions of the worlds: a new map of life on Earth. Bioscience 51(11):933–938CrossRefGoogle Scholar
- Shoji S, Nanzyo M, Dahlgren R (1993) Volcanic ash soils—genesis, properties and utilization. Elsevier, AmsterdamGoogle Scholar
- Smith D, Cannon WF, Woodruff LG, Solano F, Ellefsen KJ (2014) Geochemical and mineralogical maps for soils of the conterminous United States. In: Survey USG (ed). U.S. Geological Survey. p 386Google Scholar