Location: Grassland Soil and Water Research LaboratoryTitle: Edaphic controls of soil organic carbon in tropical agricultural landscapes
|WELL, JON - University Of Hawaii|
|CROW, SUSAN - University Of Hawaii|
|SIERRA, CARLOS - Max Planck Institute For Biogeochemistry|
|DEENIK, JONATHAN - Swedish University Of Agricultural Sciences|
|CARLSON, KIMBERLY - New York University|
|MEKI, NORMAN - Texas Agrilife Research|
Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/17/2022
Publication Date: N/A
Interpretive Summary: Predicting soil organic carbon (SOC) is difficult in tropical soils because how organic carbon changes has not been adequately described. In this study, we tried to identify storage mechanisms of SOC in a tropical landscape that had 100 years of similar soil inputs and agricultural management under production of sugarcane. We measured soil physical and chemical factors, SOC concentration, and SOC dynamics by soil horizon to one meter to identify soil parameters that can predict SOC. Using different statistical methods, we found that slow moving SOC was related to many soil parameters, while the fastest moving SOC was only related to soil surface charge. Our models explained most of the variability in SOC concentration and slow pool decay, but very little of the variability in fast pool decay. Further development of these relationships should improve the understanding of SOC storage mechanisms and soil C outcomes in tropical agricultural soils globally.
Technical Abstract: Predicting soil organic carbon (SOC) is problematic in tropical soils because mechanisms of SOC (de)stabilization are not resolved. We aimed to identify such storage mechanisms in a tropical soil landscape constrained by 100 years of similar soil inputs and agricultural disturbance under the production of sugarcane, a C4 grass and bioenergy feedstock. We measured soil physicochemical parameters, SOC concentration, and SOC dynamics by soil horizon to one meter to identify soil parameters that can predict SOC outcomes. Applying correlative analyses, linear mixed model (LMM) regression, model selection by AICc, and hierarchical clustering we found that slow moving SOC was related to many soil parameters, while the fastest moving SOC was only related to soil surface charge. Our models explained 78-79%, 51-57%, 7-8% of variance in SOC concentration, slow pool decay, and fast pool decay, respectively. Top SOC predictors were roots, the ratio of organo-complexed iron (Fe) to aluminum (Al), water stable aggregates (WSagg), and cation exchange capacity (CEC). Using hierarchical clustering we also assessed SOC predictors across gradients of depth and rainfall. Further development of these relationships is expected to improve understanding of SOC storage mechanisms and soil C outcomes in tropical agricultural soils globally.