Water-quality issues facing dairy farming: potential natural and built attenuation of nitrate losses in sensitive agricultural catchments

2020 ◽  
Vol 60 (1) ◽  
pp. 67
Author(s):  
Ranvir Singh ◽  
David J. Horne

Context Dairy farming will be increasingly scrutinised for its environmental impacts, in particular for its impacts on freshwater quality in New Zealand and elsewhere. Management and mitigation of high nitrate losses is one of the greatest water-quality challenges facing dairy farming in New Zealand and other countries. Management of critical flow pathways and nitrate-attenuation capacity could offer potential solutions to this problem and help maintain dairy-farming productivity, while reducing its water-quality impacts. Aims The present paper reviewed the key water-quality issues faced by dairy farming and assessed potential of emerging edge-of-paddock technologies, and catchment-scale nutrient-attenuation practices, to reduce nitrate losses from dairy farming to receiving water bodies. Methods We developed a conceptual catchment-scale modelling analysis assessing potential natural and built attenuation of nitrate losses from dairy farming in the Tararua and Rangitikei catchments (located in the lower part of the North Island, New Zealand). Key results This exploratory analysis suggests that a reduction of greater than 25% in the river nitrate loads from dairy-farming areas could potentially be achieved by spatially aligning dairy land with areas of high subsurface nitrate-attenuation capacity, and by managing critical flow pathways using innovative edge-of-field technologies such as controlled drainage, drainage-water harvesting for supplemental irrigation, woodchip bioreactors, and constructed wetlands in the study catchments. Conclusions The research findings highlighted the potential to better understand, map and effectively utilise existing natural and new built-in nitrate-attenuation capacity to significantly reduce water-quality impacts from dairy farming across environmentally sensitive agricultural catchments. This knowledge and tools could help farmers close the gap between what can be achieved with current, in-field mitigation practises and the nitrogen-loss allocation imposed by regulatory authorities. Implications However, the research findings presented here are based on a coarse-scale, conceptual modelling analysis, and therefore further research is recommended to develop tools and practices to better understand, map and effectively utilise existing natural and new built-in nitrogen attenuation capacity at farm-scale to achieve productive and environmentally friendly pastoral dairy farming across agricultural landscapes.

2021 ◽  
Author(s):  
◽  
Martha Trodahl

<p>Over the last 50 years freshwater and marine environments have become severely impaired due to contamination from pathogens, heavy metals, sediment, industrial chemicals and nutrients (MEA 2005b). In many countries, including New Zealand, increased nitrogen (N) and phosphorus (P) loading to terrestrial and freshwater environments from diffuse nutrient sources are of particular concern (MEA 2005a; PCE 2015b; Steffen et al. 2015) and many governments now mandate control of diffuse nutrient loss to water. Water quality models are invaluable tools that can assist with decision making around this widespread issue through exploration of the current situation and future scenarios.  Many water quality models exist, functioning at a variety of temporal and spatial scales and varying in detail and complexity. However, few, if any, simultaneously represent sub-field to catchment scale processes and outcomes, both of which are required to fully address water quality issues associated with diffuse nutrient sources. Those that do, likely require extensive time and expertise to operate. Water quality models embedded in the Land Utilisation and Capability Indicator (LUCI), an ecosystem service decision support framework, offer the opportunity to overcome these limitations. Being highly spatially explicit, yet straightforward to use, they can inform and assist individual land owners, catchment managers and other stakeholders with planning, decision making and management of water quality at sub-field to landscape scale.  To model diffuse nutrient losses LUCI, like many catchment scale water quality models, requires some form of estimated nutrient loss, or export coefficient, from land units within the catchment of interest. To be representative export coefficients must consider climate, soil, topography, and land cover and management variables. A number of methods of export coefficient derivation exist, although generally they consider only very limited geo-climatic, land cover and land management variables.  The principal aim of this study is development of algorithms capable of calculating New Zealand site specific N and P export coefficients from detailed geo-climatic, land cover and land management variables, for application in LUCI water quality models. Algorithms for pastoral land cover are developed from a large dataset comprising real pastoral farm input and output data from nutrient budgeting model OVERSEER. Algorithms are extended to land covers other than pasture, albeit in a limited manner. This is achieved through rescaling of the pastoral algorithms to account for relative differences in literature reported N and P losses from pasture and a variety of other New Zealand land covers. Application of the developed algorithms in LUCI water quality models results in positioning of export coefficients at the DEM grid square scale (≤ 15 m x 15 m for New Zealand). In addition, intra-basin configuration is considered in LUCI, at the same grid square scale, as water and nutrient flows are cascaded through the catchment. Application of the export coefficient calculating algorithms are applied to two contrasting New Zealand catchments. Tuapaka catchment, an 85ha agricultural foothill catchment in Manawatu, North Island, and Lake Rotorua catchment, a 502 km2 volcanic, mixed land cover catchment in Bay of Plenty, North Island.  This research is supported by Ravensdown, a farmer owned co-operative, which plans to use LUCI extensively to advise and assist farmers with water quality issues. The ability to model mitigation strategies in LUCI is an important capability. Therefore, this research also includes a review of five particularly important on-farm mitigation strategies, which will later be used by the wider LUCI development team to assist with better parameterisation and improved performance of mitigation options in LUCI.  Application of the developed algorithms at farm to catchment scale in LUCI results in considerably more nuanced, detailed maps and data showing N and P sources and pathways, compared to LUCI’s previously used ‘one export coefficient per land cover’ approach. Although results indicate absolute nutrient loss values are not always ‘correct’ compared to either OVERSEER predictions or in-stream water quality measurements, these differences appear comparable to those seen with similar water quality models. In addition, the issue of representativeness of OVERSEER predictions and in-stream water quality measurements exists.  Nevertheless improvement to absolute predictions is always an aim. This research indicates further improvements to LUCI water quality predictions could result from refinement of both pastoral and other land cover algorithms, and from improved representation of attenuation processes in LUCI, including groundwater representation. However, lack of measured on-land and in-stream N and P loss data is a major challenge to both algorithm refinement and to evaluation of results. In addition, more detailed spatial data would provide more nuanced results from algorithm application.  Although the algorithm application context in this research is LUCI water quality models applied in New Zealand, this does not preclude application of the developed algorithms in other export coefficient based, catchment scale water quality models. Using spatial data pertaining to climate, soil, topographic and land management variables, land units of combined variables can be identified and the algorithms applied, resulting in explicitly positioned export coefficients that can be fed into the catchment scale water quality model of interest. Therefore, developments made here potentially represent a wider contribution to catchment scale modelling using export coefficients.</p>


2015 ◽  
Vol 55 (7) ◽  
pp. 856 ◽  
Author(s):  
M. R. Scarsbrook ◽  
A. R. Melland

The scale and intensity of dairy farming can place pressure on our freshwater resources. These pressures (e.g. excessive soil nutrient concentrations and nitrogen excretion) can lead to changes in the levels of contaminants in waterways, altering the state and potentially affecting the uses and values society ascribes to water. Resource management involves putting in place appropriate responses to address water-quality issues. In the present paper, we highlight trends in the scale and extent of dairying in Australia and New Zealand and describe water-quality pressures, state, impacts and responses that characterise the two countries. In Australia and New Zealand, dairy farming has become increasingly intensive over the past three decades, although the size of Australia’s dairy herd has remained fairly static, while New Zealand’s herd and associated excreted nitrogen loads have nearly doubled. In contrast, effluent management has been improved, and farm waterways fenced, in part to reduce pressure on freshwater. However, both countries show a range of indicators of degraded water-quality state. Phosphorus and nitrogen are the most common water-quality indicators to exceed levels beyond the expected natural range, although New Zealand also has a significant percentage of waterways with faecal contaminants beyond acceptable levels for contact recreation. In New Zealand, nitrate concentrations in waterways have increased, while phosphorus and suspended sediment concentrations have generally decreased over the past decade. Water quality in some coastal estuaries and embayments is of particular concern in Australia, whereas attention in New Zealand is on maintaining quality of high-value lakes, rivers and groundwater resources, as well as rehabilitating waterbodies where key values have been degraded. In both Australia and New Zealand, water-quality data are increasingly being collated and reported but in Australia long-term trends across waterbodies, and spatially comprehensive groundwater-quality data have not yet been reported at national levels. In New Zealand, coastal marine systems, and particularly harbours and estuaries, are poorly monitored, but there are long-term monitoring systems in place for rivers, groundwater and lakes. To minimise pressures on water quality, there is a high reliance on voluntary and incentivised practice change in Australia. In New Zealand, industry-led practice change has been important over the past decade, but regulated environmental limits for dairy farmers are increasing. Dairy industries in both countries have set targets for reducing pressures through sustainability frameworks and accords. To address future drivers such as climate change and increasing domestic and international market demand for sustainability credentials, definitions of values and appropriate targets for waterbodies draining agricultural landscapes will be required. Environmental limits (both natural and societal) will constrain future growth opportunities for dairying and research into continued growth within limits remains a priority in both countries.


2021 ◽  
Author(s):  
◽  
Martha Trodahl

<p>Over the last 50 years freshwater and marine environments have become severely impaired due to contamination from pathogens, heavy metals, sediment, industrial chemicals and nutrients (MEA 2005b). In many countries, including New Zealand, increased nitrogen (N) and phosphorus (P) loading to terrestrial and freshwater environments from diffuse nutrient sources are of particular concern (MEA 2005a; PCE 2015b; Steffen et al. 2015) and many governments now mandate control of diffuse nutrient loss to water. Water quality models are invaluable tools that can assist with decision making around this widespread issue through exploration of the current situation and future scenarios.  Many water quality models exist, functioning at a variety of temporal and spatial scales and varying in detail and complexity. However, few, if any, simultaneously represent sub-field to catchment scale processes and outcomes, both of which are required to fully address water quality issues associated with diffuse nutrient sources. Those that do, likely require extensive time and expertise to operate. Water quality models embedded in the Land Utilisation and Capability Indicator (LUCI), an ecosystem service decision support framework, offer the opportunity to overcome these limitations. Being highly spatially explicit, yet straightforward to use, they can inform and assist individual land owners, catchment managers and other stakeholders with planning, decision making and management of water quality at sub-field to landscape scale.  To model diffuse nutrient losses LUCI, like many catchment scale water quality models, requires some form of estimated nutrient loss, or export coefficient, from land units within the catchment of interest. To be representative export coefficients must consider climate, soil, topography, and land cover and management variables. A number of methods of export coefficient derivation exist, although generally they consider only very limited geo-climatic, land cover and land management variables.  The principal aim of this study is development of algorithms capable of calculating New Zealand site specific N and P export coefficients from detailed geo-climatic, land cover and land management variables, for application in LUCI water quality models. Algorithms for pastoral land cover are developed from a large dataset comprising real pastoral farm input and output data from nutrient budgeting model OVERSEER. Algorithms are extended to land covers other than pasture, albeit in a limited manner. This is achieved through rescaling of the pastoral algorithms to account for relative differences in literature reported N and P losses from pasture and a variety of other New Zealand land covers. Application of the developed algorithms in LUCI water quality models results in positioning of export coefficients at the DEM grid square scale (≤ 15 m x 15 m for New Zealand). In addition, intra-basin configuration is considered in LUCI, at the same grid square scale, as water and nutrient flows are cascaded through the catchment. Application of the export coefficient calculating algorithms are applied to two contrasting New Zealand catchments. Tuapaka catchment, an 85ha agricultural foothill catchment in Manawatu, North Island, and Lake Rotorua catchment, a 502 km2 volcanic, mixed land cover catchment in Bay of Plenty, North Island.  This research is supported by Ravensdown, a farmer owned co-operative, which plans to use LUCI extensively to advise and assist farmers with water quality issues. The ability to model mitigation strategies in LUCI is an important capability. Therefore, this research also includes a review of five particularly important on-farm mitigation strategies, which will later be used by the wider LUCI development team to assist with better parameterisation and improved performance of mitigation options in LUCI.  Application of the developed algorithms at farm to catchment scale in LUCI results in considerably more nuanced, detailed maps and data showing N and P sources and pathways, compared to LUCI’s previously used ‘one export coefficient per land cover’ approach. Although results indicate absolute nutrient loss values are not always ‘correct’ compared to either OVERSEER predictions or in-stream water quality measurements, these differences appear comparable to those seen with similar water quality models. In addition, the issue of representativeness of OVERSEER predictions and in-stream water quality measurements exists.  Nevertheless improvement to absolute predictions is always an aim. This research indicates further improvements to LUCI water quality predictions could result from refinement of both pastoral and other land cover algorithms, and from improved representation of attenuation processes in LUCI, including groundwater representation. However, lack of measured on-land and in-stream N and P loss data is a major challenge to both algorithm refinement and to evaluation of results. In addition, more detailed spatial data would provide more nuanced results from algorithm application.  Although the algorithm application context in this research is LUCI water quality models applied in New Zealand, this does not preclude application of the developed algorithms in other export coefficient based, catchment scale water quality models. Using spatial data pertaining to climate, soil, topographic and land management variables, land units of combined variables can be identified and the algorithms applied, resulting in explicitly positioned export coefficients that can be fed into the catchment scale water quality model of interest. Therefore, developments made here potentially represent a wider contribution to catchment scale modelling using export coefficients.</p>


1999 ◽  
Vol 33 (4) ◽  
pp. 683-696 ◽  
Author(s):  
Robert J. Wilcock ◽  
John W. Nagels ◽  
Harvey J. E. Rodda ◽  
Michael B. O'Connor ◽  
Bruce S. Thorrold ◽  
...  

2016 ◽  
Vol 78 ◽  
pp. 7-10
Author(s):  
C.W. Holmes

New Zealand dairy farming has lost its competitive edge


In this first edition book, editors Jolly and Jarvis have compiled a range of important, contemporary gifted education topics. Key areas of concern focus on evidence-based practices and research findings from Australia and New Zealand. Other contributors include 14 gifted education experts from leading Australian and New Zealand Universities and organisations. Exploring Gifted Education: Australian and New Zealand Perspectives, introduced by the editors, is well organised. Jolly and Jarvis’s central thesis in their introduction is to acknowledge the disparity between policy, funding and practice in Australia and New Zealand. Specifically, in relation to Australia, they note that a coordinated, national research agenda is absent, despite recommendations published by the Australian Senate Inquiry almost 20 years ago.


1997 ◽  
Vol 35 (11-12) ◽  
pp. 325-331 ◽  
Author(s):  
S. A. Anderson ◽  
S. J. Turner ◽  
G. D. Lewis

Faecal enterococci ecology outside the host is of great relevance when using these organisms as indicators of water quality. As a complement to New Zealand epidemiological studies of bathing water quality and health risk, a study of the environmental occurrence of these organisms has been undertaken. Specific concerns over the use of enterococci derive from the unique situation in New Zealand which has few chlorinated sewage effluents, a high ratio of grazing animals to humans, and significant inputs of animal processing effluents into the environment. Human and animal faecal wastes are the main sources, with 106–107cfu/100ml found in human sewage. Analysis of domestic and feral animal faeces found enterococci in the range of 101–106cfu/g with considerable variation between species. The latter observations support the notion that a considerable proportion of the load in urban/rural catchments and waterways (typically 102–103 enterococci cfu/100ml) is derived from non-human sources. Previous studies of enterococci quiescence in marine/fresh waters indicate that they enter a non-growth phase, exposure to sunlight markedly reducing culturability on selective and non-selective media. Enterococci were also found to survive/multiply within specific non-faecal environments. Enterococci on degrading drift seaweed at recreational beaches exceeded seawater levels by 2–4 orders of magnitude, suggesting that expansion had occurred in this permissive environment with resultant potential to contaminate adjacent sand and water. These studies suggest that multiple sources, environmental persistence, and environmental expansion of enterococci within selected niches add considerable complexity to the interpretation of water quality data.


2021 ◽  
pp. 100197
Author(s):  
Adrian Fernandez-Perez ◽  
Bart Frijns ◽  
Ilnara Gafiatullina ◽  
Alireza Tourani-Rad

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