Towards an integrated global framework to assess the impacts of land use and management change on soil carbon: current capability and future vision

Abstract

Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodologies commonly underpin project‐scale carbon accounting for changes in land use and management and are used in frameworks for Life Cycle Assessment and carbon footprinting of food and energy crops. These methodologies were intended for use at large spatial scales. This can introduce error in predictions at finer spatial scales. There is an urgent need for development and implementation of higher tier methodologies that can be applied at fine spatial scales (e.g. farm/project/plantation) for food and bioenergy crop greenhouse gas (GHG) accounting to facilitate decision making in the land‐based sectors. Higher tier methods have been defined by IPCC and must be well evaluated and operate across a range of domains (e.g. climate region, soil type, crop type, topography), and must account for land use transitions and management changes being implemented. Furthermore, the data required to calibrate and drive the models used at higher tiers need to be available and applicable at fine spatial resolution, covering the meteorological, soil, cropping system and management domains, with quantified uncertainties. Testing the reliability of the models will require data either from sites with repeated measurements or from chronosequences. We review current global capability for estimating changes in soil carbon at fine spatial scales and present a vision for a framework capable of quantifying land use change and management impacts on soil carbon, which could be used for addressing issues such as bioenergy and biofuel sustainability, food security, forest protection, and direct/indirect impacts of land use change. The aim of this framework is to provide a globally accepted standard of carbon measurement and modelling appropriate for GHG accounting that could be applied at project to national scales (allowing outputs to be scaled up to a country level), to address the impacts of land use and land management change on soil carbon.

Climate-smart agriculture global research agenda: scientific basis for action

“…Such research includes use of remote sensing to analyse land-use mosaics, inventory approaches to assessing C stocks and water resources, and models to examine the potential of landuse change in different climate scenarios [196][197][198]. These techniques are being combined with farm-and field-scale data on crop performance, soil biogeochemistry and irrigation use to analyse if and how mitigation and/or adaptation strategies build food security and ecosystem services [34,[199][200][201].…”

- Comparison of Enzymatic Reactivity of Corn Stover Solids Prepared by Dilute Acid, Afex™, and Ionic Liquid Pretreatments

“…First, SOC content data for soils worldwide is needed, as explained in Smith et al [8], who provide a vision for developing these data and discuss key sources of uncertainty in their development. Soil organic matter models such as CENTURY would benefit from further calibration of default parameters, including the soil cultivation effect coefficient, with realworld data.…”

The Positive Feedback Loop between the Impacts of Climate Change and Agricultural Expansion and Relocation

“…Anthropogenic changes in the carbon storage capacity of land can also occur in the absence of land conversion. For example, soil organic carbon is lost over time through many agricultural practices [2]. In addition to land use change emissions, agriculture also contributes to climate change through its ongoing activities: the large number of domesticated ruminants has increased CH 4 emissions and, likewise, the application of fertiliser results in N 2 O emissions; both are strong GHGs.…”

“…Changes in precipitation and temperature can have both positive and negative effects. The elevated CO 2 concentration is expected to contribute positively, although it is unclear how the increased competitiveness of weeds, which is not yet included in the models, will decrease such benefits [2]. Maize and other C4 crops generally fare the worst, as they do not benefit as much from increased CO 2 levels [18].…”