Opening Our File Cabinets and Field Notes with Meta-analysis: An Approach to Overcome the Soil C Sequestration Data Void.

Ongoing concerns about the long-term sustainability of the world’s soils resources demand an improved understanding of how soil organic carbon (SOC) dynamics are impacted by land management.   Meta-analysis is a statistical tool that can help the soil and crop scientist move beyond their isolated file cabinets and field notes as it is a means to interconnect their ideas and information with the rest of the world. With a meta-analytic approach the literature is reviewed, the existing data are extracted and then used to collectively describe the variability, statistical significance, and the impact of moderator variables on a given phenomenon.  When meta-analysis is based on well described sources of information, it is possible to develop a quantitative description of the existing research area, discover convergent themes and then develop well found hypothesis.  For example, we conducted a meta-analysis to provide a comprehensive and quantitative synthesis of SOC changes following land-use conversion to cropping of two biofuel crops, Panicum virgatum (switchgrass) or Miscanthus giganteous. Data taken from 33 published studies showed there were no changes in SOC under biofuel cropping compared to previous land use.    While the results from the meta-analysis were interesting in that biofuels crops are often promoted to sequester soil carbon, our secondary findings included a realization that key data are often not reported in the paper as some studies fail to provide any information on their underlying systems.  To create the most useful outcomes, meta-analysis is contingent on a consent format in the data so that the reviewer has access to background and supporting information.  In this presentation, the need for consistent forms of data reporting as related to soil carbon will be discussed and examples from other meta-analysis will be highlighted.   Clearly, data linkages via online resources would streamline a meta-analysis approach and improve the interpretations that are possible from published information.