Significant differences were found to exist among various LC/LU's in regards to mean SOC stocks (kg m−2, 0-20cm), with the highest amounts found in Cypress Swamp (9.7), Hardwood Swamp (9.6) and Mixed Wetland Forest (7.8). Additionally, significant SOC stock differences among various soil types exist as well, with the highest mean stocks (kg m−2, 0-20cm) belonging to Saprists (12), Aquolls (9.8) and Aquepts (9.4). Geostatistical (kriging) models developed for the study area show approximately 102 - 108 Tg SOC (kg C m−2) are held within the upper 20cm of soils representing historical conditions (DS1) and 211 - 320 Tg SOC (kg C m−2) are held within the upper 20cm of soils representing current conditions (DS2), which suggests the soils in the study area have been a net sink for C during the last 40 years. Highest SOC stock sequestration rates were observed in Hardwood/Cypress Swamp (51 g C m−2 yr−1) and the lowest observed in Xeric Upland Forest ( -129 g C m−2 yr−1). Additionally, site remaining in Row/field Crop lost SOC ( -2g C m−2 yr−1) on average. Interestingly, three out of four classes switching to Urban resulted in net gains of SOC stocks. Geostatistical models improved the knowledge of the spatial distribution and variability of SOC in the study area with implications for SOC cycling, land management, environmental conservation and policy decisions.

Forest soils contribute to a significant portion of the world's carbon flux due to both natural and anthropogenic changes. In terms of human management of carbon pools, forest soil organic matter (SOM) is important because it potentially stores carbon more permanently than living vegetation. Yet, this potential is poorly understood or managed for because of the difficulty in measuring changes in SOM pools over time and space. Modeling combined with intensive field sampling can help overcome these limitations because it extracts from empirically observed relationships to account for the components of SOM formation (topography, time, parent material, organisms and climate [fns2] ). This study utilizes intensive field data, statistical models and process-based ecosystem models to investigate the spatial distribution and dynamics of soil organic carbon dynamics in two contrasting ecosystems--the northern hardwood forest in the Green Mountains, VT and the tabonuco forest in the Luquillo Experimental Forest, PR. In both forests landscape position emerged as the dominate factor in explaining SOM distribution. In Vermont, additional variation was explained by aspect and slope and in Puerto Rico additional variation was explained by landscape factors interrelated to soil drainage. Process-based modeling proved to be a useful management and experimental tool in cases were empirical approaches were impractical for both forests. In Vermont, three ecosystem models demonstrated a substantial reduction of soil organic carbon and harvestable biomass due to the removal of woody carbon by logging after 240 years of rotations. In Puerto Rico, the Century model showed that changes in litter quality and quantity were not likely responsible in explaining landscape level SOM differences. Overall, well drained soils located in colder climates stored the highest SOM whereas poorly drained and highly disturbed soils in steep humid climates stored the lowest SOM. This research demonstrates that although SOM amounts are highly variable over many spatial and temporal scales, intuitive relationships are borne out with modeling tools and by careful investigation of the five soil forming factors. Results also raise questions about how these ecosystems and their SOM pools may change in response to changing climate conditions of the future.

Carbon stored in soils represents the largest terrestrial carbon pool and factors affecting this will be vital in the understanding of future atmospheric CO2 concentrations. This book provides an integrated view on measuring and modeling soil carbon dynamics. Based on a broad range of in-depth contributions by leading scientists it gives an overview of current research concepts, developments and outlooks and introduces cutting-edge methodologies, ranging from questions of appropriate measurement design to the potential application of stable isotopes and molecular tools. It includes a standardised soil CO2 efflux protocol, aimed at data consistency and inter-site comparability and thus underpins a regional and global understanding of soil carbon dynamics. This book provides an important reference work for students and scientists interested in many aspects of soil ecology and biogeochemical cycles, policy makers, carbon traders and others concerned with the global carbon cycle.

Few topics cut across the soil science discipline wider than research on soil carbon. This book contains 48 chapters that focus on novel and exciting aspects of soil carbon research from all over the world. It includes review papers by global leaders in soil carbon research, and the book ends with a list and discussion of global soil carbon research priorities. Chapters are loosely grouped in four sections: § Soil carbon in space and time § Soil carbon properties and processes § Soil use and carbon management § Soil carbon and the environment A wide variety of topics is included: soil carbon modelling, measurement, monitoring, microbial dynamics, soil carbon management and 12 chapters focus on national or regional soil carbon stock assessments. The book provides up-to-date information for researchers interested in soil carbon in relation to climate change and to researchers that are interested in soil carbon for the maintenance of soil quality and fertility. Papers in this book were presented at the IUSS Global Soil C Conference that was held at the University of Wisconsin-Madison, USA.

Results showed that five sites had different spatial structure - Hardwood Hammock and Forest and Improved Pasture demonstrated both large variation at both coarse scale (67 and> 200 m) and very fine scale (2 m). Sandhill, Pineland and Dry Prairie were dominated by variation at very fine scales (2 and 7 m). All the five sites showed large variability at very fine scales, indicating the close coupling of SOC variation to structure and composition of vegetation. Lastly, the SOC change coupled with LULC and climatic factors over the past four decades was studied. Significant SOC accumulation was observed between 1965-1996 and 2008-2009 and concomitant LULC and LULC change significantly affected SOC change, less so climatic factors. The study improved the knowledge of the spatial and temporal variation of SOC in the complex soil-landscape continuum of Florida with implications for carbon cycling and sequestration, land resource management, and ecosystem service assessment.

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 512 million unique, spatially-explicit crop growth simulations from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM), we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-determined meta-models are highly accurate (R2 = .97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models accurately capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

GlobalSoilMap: Basis of the global spatial soil information system contains contributions that were presented at the 1st GlobalSoilMap conference, held 7-9 October 2013 in Orléans, France. These contributions demonstrate the latest developments in the GlobalSoilMap project and digital soil mapping technology for which the ultimate aim is to produce a high resolution digital spatial soil information system of selected soil properties and their uncertainties for the entire world. GlobalSoilMap: Basis of the global spatial soil information system aims to stimulate capacity building and new incentives to develop full GlobalSoilMap products in all parts of the world.