Increasing the quantity of carbon stored in agricultural soils has the potential to offset emissions of greenhouse gases to the atmosphere, while soil carbon losses would further add to those emissions.
However, realising this mitigation potential is technically challenging when soil carbon stocks are already high (as they are in New Zealand), potential changes in soil carbon are small and spatial variability is high.
The current NZAGRC programme has three distinct components:
(1) testing specific management practices that may increase the long term soil carbon store in field situations;
(2) developing and using models to predict how a range of management practices may influence long and short tem soil carbon storage; and
(3) identifying those factors that influence the stability of current or newly added soil carbon.
Dr David Whitehead, Manaaki Whenua - Landcare Research (2010-present) Professor Frank Kelliher, AgResearch (2010-2017)
Producing a farm-scale soil carbon map
The study site is the Massey University Tuapaka hill country farm. This 470-ha farm, 15 km NW of Palmerston North, consists of two distinctive management blocks – the terrace flats and the hill block.
A legacy soil map was digitised and used to produce soil and parent material datalayers for the digital soil mapping exercise. LIDAR survey data were provided to the project, at a resolution of approximately 1 measurement per square metre in open ground, and processed into a 5-m resolution digital elevation model (DEM). This model was then further processed into additional terrain attribute layers, including slope, aspect, relative wetness (displayed as a wetness index), geomorphological features , and global solar irradiation layers (Figure 5).
These environmental datalayers are used twice in the digital soil mapping process. First, they are used to determine where initial soil carbon measurements should be obtained. This initial sampling method ensures that soils are sampled to proportionally represent the full range of topographic features in the target area and cover the full range of likely soil organic carbon stocks. Second, the datalayers are used together with soil carbon spot measurements to predict a soil carbon map.
Soil cores were collected from 50 stratified positions, and soil carbon stocks were determined at these positions. This dataset was then combined in the spatial modelling step to produce the soil carbon map (Figure 5).
Figure 5: The elevation map, derived from the LIDAR survey, is analysed to give, for example, slope, aspect, wetness index, geomorphic units and solar irridiation maps. Digital soil mapping uses these maps with any other relevant data, such as legacy soil maps, climate layers and geology maps, together with soil carbon spot measurements, to develop a prediction model and produce the soil carbon map.