Water table response to an experimental alley farming trial: dissecting the spatial and temporal structure of the data

Abstract

Clearing vegetation for traditional agriculture diminishes native habitat and reduces plant transpiration, leading to increased groundwater recharge and onset of dryland salinization due to rising groundwater and mobilization of salt stores in the soil profile. This change in hydrology and salinity can also negatively affect biodiversity in many semiarid regions. Alternating native perennial tree belts with mono-species agriculture within the tree belt alleys is one possible system that can provide recharge control and recover some of the ecosystem services of degraded agricultural landscapes. To assess the effect of this agroforestry technique on groundwater levels, an alley farming trial was established in 1995, incorporating different combinations of belt width, alley width, and revegetation density. Transects of piezometers within each design have been monitored from October 1995 to January 2008. The data set consisted of 70 piezometers monitored on 39 dates. Two trends were observed within the raw data: An increase in water table depth with time and an increase in the range of depths monitored at the site were clearly discernible. However, simple hydrograph analysis of the data has proved unsuccessful at distinguishing the effect of the tree belts on the water table morphology. The statistical techniques employed in this paper to show the effect of the experiment on the water table were variation partitioning, principal coordinates of neighbor matrices (PCNM), and canonical redundancy analysis (RDA). The environmental variables (alley farming design, distance of piezometer from the tree belt, and percentage vegetation cover including edge effect) explained 20-30% of the variation of the transformed and detrended data for the entire site. The spatial PCNM variables explained a further 20-30% of the variation. Partitioning of the site into a northern and southern block increased the proportion of explained variation for the plots in the northern block. The spatial PCNM variables and vegetation cover remained the most significant variables. The PCNM analysis revealed no spatial pattern that could be attributed to the trial. The high proportion of unexplained variation may be due to site variables that have not been considered in this study.