Quantifying contaminant losses to water from pastoral landuses in New Zealand II. The effects of some farm mitigation actions over the past two decades

Monaghan, Rachel; Manderson, Andrew; Basher, Les; Spiekermann, Raphael; Dymond, J. R.; Smith, L. C.; Muirhead, Richard W.; Burger, David F.; McDowell, R. W.

doi:10.1080/00288233.2021.1876741

Cited by 24 publications

(20 citation statements)

References 46 publications

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“…In New Zealand, a recent analysis found that 43% of agricultural land was in catchments where the current load exceeded the maximum allowed 35 . Other work 36 estimated that, had farming mitigation practices over 1995–2015 not been adopted, 45% more N (largely as nitrate–N) would have been lost. Despite these efforts, the expansion of intensive land uses has increased N loads by 25% nationally 36 .…”

Section: Discussionmentioning

confidence: 99%

“…Other work 36 estimated that, had farming mitigation practices over 1995–2015 not been adopted, 45% more N (largely as nitrate–N) would have been lost. Despite these efforts, the expansion of intensive land uses has increased N loads by 25% nationally 36 . Where it was assumed all actions to mitigate N losses were adopted, additional modelling showed that future N loads could decrease by about a third 37 , reducing the area still exceeding the maximum allowed to about 5% 38 .…”

Section: Discussionmentioning

confidence: 99%

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The implications of lag times between nitrate leaching losses and riverine loads for water quality policy

McDowell

Simpson

Ausseil

et al. 2021

Sci Rep

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Understanding the lag time between land management and impacts on riverine nitrate–nitrogen (N) loads is critical to understand when action to mitigate nitrate–N leaching losses from the soil profile may start improving water quality. These lags occur due to leaching of nitrate–N through the subsurface (soil and groundwater). Actions to mitigate nitrate–N losses have been mandated in New Zealand policy to start showing improvements in water quality within five years. We estimated annual rates of nitrate–N leaching and annual nitrate–N loads for 77 river catchments from 1990 to 2018. Lag times between these losses and riverine loads were determined for 34 catchments but could not be determined in other catchments because they exhibited little change in nitrate–N leaching losses or loads. Lag times varied from 1 to 12 years according to factors like catchment size (Strahler stream order and altitude) and slope. For eight catchments where additional isotope and modelling data were available, the mean transit time for surface water at baseflow to pass through the catchment was on average 2.1 years less than, and never greater than, the mean lag time for nitrate–N, inferring our lag time estimates were robust. The median lag time for nitrate–N across the 34 catchments was 4.5 years, meaning that nearly half of these catchments wouldn’t exhibit decreases in nitrate–N because of practice change within the five years outlined in policy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The implications of lag times between nitrate leaching losses and riverine loads for water quality policy

McDowell

Simpson

Ausseil

et al. 2021

Sci Rep

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“…We attributed improving DRP trends to high mean dairy cow and low mean sheep stocking intensity (PC1 in Table 5). A plausible explanation for this may be that the implementation of mitigation measures to reduce phosphorus loss has been growing rapidly on dairy farms, which have had the greatest pressure to improve land use management practices under the Dairying and Clean Streams Accord and its successors (Bewsell et al 2007;Scarsbrook and Melland 2015;Monaghan et al 2021). The reasons for degrading DRP trends in 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 association with high mean beef cow and low mean dairy cow stocking intensity (PC8) and degrading TP trends in association with increasing total stocking intensity (PC4) are unclear, but may reflect slower implementation of mitigation measures on non-dairy farms.…”

Section: Discussionmentioning

confidence: 99%

“…Where changes in catchment area occupied by grazed grassland have occurred, they are generally associated with commensurate changes in area of plantation forests, with grazed grassland in some areas being replaced by forest and vice versa (New Zealand Government 2020b). In addition, within each of the four main types of pastoral farming (sheep, dairy, beef and deer farming) and plantation forestry, there have been differences in land use practices, such as the types and rate of adoption of mitigation measures (Monaghan et al 2021).…”

Section: Land Use Indicatorsmentioning

confidence: 99%

“…In response to public concerns about water quality impacts, regulatory reforms have been enacted by the New Zealand government over the past decade that aim to prevent and reverse river water quality degradation associated with agriculture , 2020a. These reforms increased requirements for the agriculture sector to reduce impacts on aquatic receiving environments through measures such as improved management of fertiliser, livestock effluent and irrigation water, reducing stock access to streams, riparian protection and tree planting on erodible hill country (Monaghan et al 2021). In addition, there have been industry-led initiatives such as the Dairying and Clean Streams accord, which requires the exclusion of dairy cattle from channels and riparian zones of perennial rivers greater than 1 m wide (Bewsell et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Attribution of river water-quality trends to agricultural land use and climate variability in New Zealand

Snelder¹,

Larned

Monaghan

et al. 2021

Mar. Freshwater Res.

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Trends at 1051 river monitoring sites across New Zealand incrementing annually for time windows of 10 and 20 years over the 28-year period ending 2017 were assessed from regular observations of six water quality variables. Between-site variation in trend strength and direction was modelled as a function of an indicator based on the Southern Oscillation Index (SOI) and the mean of and changes to catchment: (1) stocking intensity associated with pastoral livestock; and (2) area associated with plantation forest. The SOI indicator made consistent contributions to the models for the 10-year windows, but the land use indicators did not, indicating that land use signals were generally swamped by the effects of climate variability at this timescale. Some land use indicators made consistent and certain contributions to the models for the 20-year time windows. Depending on the water quality variable, some land use indicators were associated with both water quality improvement and degradation. The relationships were generally consistent with plausible explanations including changes in land use, land use intensity and land management practices. Robust attribution of water quality changes to changes to specific agricultural land uses will enable the development of precise and effective policies to achieve water quality improvement.

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Escherichia coli runoff from sheep and dairy cow grazed pasture: A plot scale simulation

Muirhead

2023

J of Env Quality

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Animal agriculture is recognized as a key source of fecal microbial impacts on water quality and associated risks to human health. Most of the research effort has focused on losses of fecal microbes from cow/cattle feces with little research effort on sheep fecal risks. The literature on fecal microbial risks from pasture is complicated by the fact that experiments are carried out in different environments leading to difficulties in making direct comparisons between sheep and cow/cattle losses from pasture areas. In this study, a plot scale simulation was conducted on the same pasture plots, using simulated rainfall to generate comparable runoff conditions, and using simulated grazing to create similar relative stocking rates. The Escherichia coli concentrations in the runoff were similar from simulated or natural rainfall events. At an equivalent stocking rate, the E. coli runoff concentrations from the sheep grazed pastures were four times higher than the cow grazed pasture. These results show that at an equivalent stocking rate, the E. coli runoff risk from sheep grazed pasture is higher than for cow grazed pasture. Further research is needed to understand the relative impacts of different grazing species of animals as well as stocking rate or management effects on these relative risks to water quality.

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Quantifying contaminant losses to water from pastoral landuses in New Zealand II. The effects of some farm mitigation actions over the past two decades

Cited by 24 publications

References 46 publications

The implications of lag times between nitrate leaching losses and riverine loads for water quality policy

The implications of lag times between nitrate leaching losses and riverine loads for water quality policy

Attribution of river water-quality trends to agricultural land use and climate variability in New Zealand

Escherichia coli runoff from sheep and dairy cow grazed pasture: A plot scale simulation

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