Which agricultural management interventions are most influential on soil organic carbon (using time series data)?
Loss of soil organic carbon (SOC) from agricultural land is identified as one of the major threats to soils, as it influences both fertility and the production of ecosystem services from agriculture. Losses of SOC across regions are often determined by monitoring in different land use systems. Results from agricultural field experiments can reveal increasing SOC stocks after implementation of specific management practices compared to a control, though in time series experiments the relative rate of change is often negative and implying an overall loss. Long-term agricultural field experiments are indispensable for quantifying absolute changes in SOC stocks under different management regimes. Since SOC responses are seldom linear over time, time series data from these experiments are particularly valuable.
This systematic review is based on studies reporting time series data collated in a recently completed systematic map on the topic restricted to the warm temperate climate zone and the snow climate zone. These 53 studies were identified and selected systematically according to CEE guidelines. An update of the original search for studies will be repeated using Web of Science and Google Scholar to include newly published academic and grey literature in the time since the original search was performed in September 2013. Studies will be subject to critical appraisal of the internal and external validity, followed by full data extraction (meta-data describing study settings and quantitative study results). Where possible, studies will be included in a quantitative synthesis using time series meta-analytical approaches. The implications of the meta-analytical findings will be discussed in terms of policy, practice and research along with a discussion of the nature of the evidence base.
Agriculture, Tillage, Fertilization, Crop rotation, Amendment, Farming, Conservation, Carbon sequestration, Long-term field experiments, Time series data
Review in Progress