Assessing the effects of agricultural management practices on carbon fluxes: Spatial variation and the need for replicated estimates of Net Ecosystem Exchange

Abstract

Replicated measurements of Net Ecosystem CO 2 Exchange (NEE) were made on four arable plots using two eddy covariance (EC) towers that were moved at regular intervals. Replicated plots were created by dividing a large arable field into four approximately equal areas (∼2.5 ha). Regular adjustments of the EC measurement height (1.5–1.9 m) due to seasonal changes in canopy height ensured that the analyzed fluxes were derived from the individual plots. This was sufficient to make high-quality flux measurements with the energy balance accounting for up to 96% of the available energy in the system. The replicated experiment included two treatment plots managed with conventional tillage practices (CON) and two plots managed with non-inversion tillage (NIT). The mean annual NEE (aNEE) over all plots and years showed a carbon uptake of –163 g [C] m −2 y −1. Plot to plot variability ranged between 9 and 70 g [C] m −2 y −1. Differences in leaf area index (LAI) were the most likely cause of plot to plot variability. For the majority of the experiment spatial variation was greater than treatment effects and provided over half the total annual uncertainty, demonstrating the need to address spatial variability when assessing aNEE using EC techniques. Interannual variation in the measured fluxes was greater than the plot-to-plot or the treatment variations, most likely due to differences in water availability, although this could be confounded by changes in spatial variability due to other environmental factors or management operations. The proportionally large uncertainty associated with spatial and interannual variability when compared to the measured fluxes, suggests that NEE estimates are, in the short term, unlikely to result in sufficiently accurate estimates of net biome productivity (NBP) for assessing the slow accumulation/loss of SOC associated with altered tillage management without a much larger number of replicated plots. Whilst the approach used has limited statistical power it could be modified to accommodate a large number of replicates to provide more robust estimates of treatment differences.