The NZAGRC is committed to providing information regarding agricultural greenhouse gases research and overview information.

Below are a list of publications and reports from a variety of sources that may be useful if you're interested in agricultural greenhouse gases. They range from information for those who have a general interest in greenhouse gas mitigation options and technologies through to very specific science papers on the various gases, technologies and mitigation solutions.

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Stocks, composition and vulnerability to loss of soil organic carbon predicted using mid-infrared spectroscopy

Baldock, J. A., Beare, M. H., Curtin, D., & Hawke, B. (2018). Stocks, composition and vulnerability to loss of soil organic carbon predicted using mid-infrared spectroscopy. Soil Research, 56(5), 468-480. 

Developing a routine and cost effective capability for measuring soil organic carbon (SOC) content and composition will allow identification of land management practices with a potential to maintain or enhance SOC stocks. Coupling SOC content data and mid-infrared (MIR) spectra through the application of partial least-squares regression (PLSR) analyses has been used to develop such a prediction capability. The objective of this study was to determine whether MIR/PLSR analyses provide accurate estimates of the content and composition of SOC that can be used to quantify SOC stocks and its potential vulnerability to loss. Soil was collected from a field trial incorporating a range of land use (pasture, arable cropping and bare fallow) and tillage (intensive, minimum and no tillage) treatments over a nine-year period. The SOC content was measured by dry combustion analysis. Particulate organic carbon was separated from other forms of carbon on the basis of particle size (SOC in the >50 µm fraction). Resistant organic carbon was quantified using solid-state 13C nuclear magnetic resonance. The MIR/PLSR algorithms were successfully developed to predict the natural logarithms of the contents of SOC and POC in the collected soils. With initial calibration, a single MIR analysis could be used in conjunction with PLSR algorithms to predict the content of SOC and its allocation to component fractions. The MIR/PLSR predicted SOC contents provided reliable estimates of the impact of agricultural management on the 0–25-cm SOC stocks, as well as an indication of the vulnerability of SOC to loss. Development of this capability will facilitate the rapid and cost effective collection of SOC content data for detecting the impact of agricultural management treatments on SOC stocks, composition and potential vulnerability to change.

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