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Auckland, New Zealand

Pronger J.,University of Waikato | Schipper L.A.,University of Waikato | Hill R.B.,Waikato Regional Council | Campbell D.I.,University of Waikato | McLeod M.,Landcare Research
Journal of Environmental Quality

The drainage and conversion of peatlands to productive agroecosystems leads to ongoing surface subsidence because of densification (shrinkage and consolidation) and oxidation of the peat substrate. Knowing the rate of this surface subsidence is important for future land-use planning, carbon accounting, and economic analysis of drainage and pumping costs. We measured subsidence rates over the past decade at 119 sites across three large, agriculturally managed peatlands in the Waikato region, New Zealand. The average contemporary (2000s-2012) subsidence rate for Waikato peatlands was 19 ± 2 mm yr-1 (± SE) and was significantly less (p = 0.01) than the historic rate of 26 ± 1 mm yr-1 between the 1920s and 2000s. A reduction in the rate of subsidence through time was attributed to the transition from rapid initial consolidation and shrinkage to slower, longterm, ongoing oxidation. These subsidence rates agree well with a literature synthesis of temperate zone subsidence rates reported for similar lengths of time since drainage. A strong nonlinear relationship was found between temperate zone subsidence rates and time since initial peatland drainage: Subsidence (mm yr-1) = 226 × (years since drained)-0.59 (R2 = 0.88). This relationship suggests that time since drainage exerts strong control over the rate of peatland subsidence and that ongoing peatland subsidence rates can be predicted to gradually decline with time in the absence of major land disturbance. © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. Source

Vant B.,Waikato Regional Council
New Zealand Journal of Forestry

The Council has operated a water-quality monitoring program at Lake Taupo since 1994. A deep water site near the middle of the lake is visited every two to four weeks, and water samples are collected and field measurements made. The graphs show the monthly changes in water quality between 1995 and 2011. Much of the rain falling on the Taupo catchment area of 2,800 square kilometers percolates through the soil and is stored underground as ground water, in some cases for many years, before finally entering the streams and then the lake. The groundwater therefore contains some of the nitrogen leached from historic land use practices but which has not yet entered the lake. When the variation to the plan was developed it was anticipated that, despite capping, the loads of nitrogen entering the lake in its inflows would continue to increase until the offsetting began to take effect. It was expected that it would take several decades or more before the full effects of intervention would be seen in the lake. Source

Barnett A.L.,University of Waikato | Schipper L.A.,University of Waikato | Taylor A.,Waikato Regional Council | Balks M.R.,University of Waikato | Mudge P.L.,Landcare Research
Agriculture, Ecosystems and Environment

A previous temporal sampling study of New Zealand soils under different grazing systems indicated that soils on flat land under dairy farming had lost significant amounts of C and N in the last few decades, while soils under drystock farming on flat land had not. This result suggested that dairy farms would have lower soil C stocks than adjacent drystock farms. To test this hypothesis, we sampled 25 adjacent dairy and drystock farms to 0.6m depth and analysed samples for C, N and soil dry bulk density by horizon. Paired sampling sites were on average 108m apart, on the same soil with similar slope, aspect and topography and had been in that farming system for at least 10 years prior. The average stocking rate for dairy farms (24 stock units ha-1) was higher (P<0.01) than drystock farms (14 stock units ha-1). The mean total C and total N stocks for the whole soil profile (0-0.6m) were 173tCha-1 and 15.7tNha-1 for the dairy farms and 183tCha-1 and 16.1tNha-1 for the drystock farms and these were not significantly different. However, when the soil horizons were considered separately, the A horizon of dairy farms had significantly lower C (8tCha-1, P<0.05) than drystock farms. The A horizon thickness under dairy farming was also shallower (P<0.05) with a greater soil dry bulk density (P<0.05) than the drystock farms indicating soil compaction, presumably due to higher stocking rates and heavier animals on dairy farms. Changes in soil dry bulk density and A horizon depth offset one another and the total mass of soil sampled from the A horizons was the same (0.14±0.01tm-2). Therefore, the significant difference in soil C of the A horizon was likely to be a consequence of land management rather than as a result of sampling different masses of soil. Lower soil C content of the A horizon in this paired site study is consistent with an earlier sampling using temporal comparisons. We do not know the causes for these differences in C, but they might be linked to the higher stocking rates of dairy systems, where large dairy cows exert greater physical pressure on the soil, consume more above ground biomass, and deposit more intense urine patches that have been linked to solubilisation of soil C. © 2013 Elsevier B.V. Source

Collier K.J.,Waikato Regional Council | Collier K.J.,University of Waikato | Olsen A.R.,U.S. Environmental Protection Agency
Marine and Freshwater Research

We investigated outcomes of three monitoring networks for assessing ecological character and condition of wadeable streams, Waikato region, New Zealand. Site selection was based on professional judgment, stratification within categories of watershed characteristics, or on using an unequal-probability survey design. The professional-judgment network, stratified network and all site analyses included more ≥4th-order streams than for the probability-network survey-design estimates Professional-judgment and stratified network sites and survey-design analyses incorporated higher-quality catchments with coarser substrates. Cumulative frequency distributions indicated that the stratified and/or judgmental networks yielded fewer taxa than did the probability network, and that the stratified network provided lower estimates of the macroinvertebrate community index (MCI). Compared with the probability-network survey-design analysis, the stratified network site analysis underestimated percentage stream length classed as 'Excellent' by the quantitative MCI, and the professional-judgment site and survey-design analyses overestimated the percentage classed 'Fair' by the average score per metric. We conclude that deriving reliable estimates of wadeable stream character and condition requires (1) clearly defining and quantifying the target population for which inferences will be drawn, (2) accounting for probability of site selection and (3) optimising spatial representation across dominant stressor gradients. © 2013 CSIRO. Source

Woodward S.J.R.,Lincoln Agritech Ltd. | Stenger R.,Lincoln Agritech Ltd. | Hill R.B.,Waikato Regional Council
Transactions of the ASABE

While analysis of river water quality time series data alone allows observation of means, variances, trends, and seasonality, it cannot elucidate the catchment mechanisms responsible for these observations. Incorporating river flow data into the analysis allows additional insight to be gained into the mechanisms driving water quality change. Twenty-year series of monthly water quality samples were analyzed alongside high-resolution flow records in 26 catchments across the agriculturally dominated Waikato region of New Zealand. Concentration-discharge relationships indicated the importance of near-surface flow paths in transporting nitrogen and non-dissolved phosphorus species from the land into rivers. Dissolved phosphorus, on the other hand, appears to be discharged primarily in deeper groundwater carrying higher concentrations of geogenic origin. Subsequent data stratification was able to explain the origin of nitrate or phosphorus trends in some catchments as being due to either historical or recent land use changes. These results highlight the value of combined analysis of water quality data with river flow records. © 2016 American Society of Agricultural and Biological Engineers. Source

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