Bioavailable Cu can influence nitrification rate in New Zealand dairy farm soils

2021 ◽  
Author(s):  
Dumsane Themba Matse ◽  
Paramsothy Jeyakumar ◽  
Peter Bishop ◽  
Christopher W. N. Anderson
Keyword(s):  
New Zealand ◽  
Dairy Farm ◽  
Nitrification Rate ◽  
Farm Soils

Tree Species for Recovering Nitrogen from Dairy-Farm Effluent in New Zealand

2001 ◽  
Vol 30 (3) ◽  
pp. 1064-1070 ◽  
Author(s):  
Jon K.F. Roygard ◽  
Brent E. Clothier ◽  
Steve R. Green ◽  
Nanthi S. Bolan
Keyword(s):  
New Zealand ◽  
Tree Species ◽  
Dairy Farm

scholarly journals Still ‘dairy farm fever’? A Bayesian model for leptospirosis notification data in New Zealand

2021 ◽  
Vol 18 (175) ◽  
pp. 20200964
Author(s):  
Jackie Benschop ◽  
Shahista Nisa ◽  
Simon E. F. Spencer
Keyword(s):  
New Zealand ◽  
Public Health Surveillance ◽  
Dairy Farm ◽  
Nucleic Acid Detection ◽  
Dairy Farmers ◽  
Occupational Groups ◽  
Specific Occupation ◽  
Health Surveillance Data ◽  
Notification Data ◽  
Important Evidence

Routinely collected public health surveillance data are often partially complete, yet remain a useful source by which to monitor incidence and track progress during disease intervention. In the 1970s, leptospirosis in New Zealand (NZ) was known as ‘dairy farm fever’ and the disease was frequently associated with serovars Hardjo and Pomona. To reduce infection, interventions such as vaccination of dairy cattle with these two serovars was implemented. These interventions have been associated with significant reduction in leptospirosis incidence, however, livestock-based occupations continue to predominate notifications. In recent years, diagnosis is increasingly made by nucleic acid detection which currently does not provide serovar information. Serovar information can assist in linking the recognized maintenance host, such as livestock and wildlife, to infecting serovars in human cases which can feed back into the design of intervention strategies. In this study, confirmed and probable leptospirosis notification data from 1 January 1999 to 31 December 2016 were used to build a model to impute the number of cases from different occupational groups based on serovar and month of occurrence. We imputed missing occupation and serovar data within a Bayesian framework assuming a Poisson process for the occurrence of notified cases. The dataset contained 1430 notified cases, of which 927 had a specific occupation (181 dairy farmers, 45 dry stock farmers, 454 meatworkers, 247 other) while the remaining 503 had non-specified occupations. Of the 1430 cases, 1036 had specified serovars (231 Ballum, 460 Hardjo, 249 Pomona, 96 Tarassovi) while the remaining 394 had an unknown serovar. Thus, 47% (674/1430) of observations had both a serovar and a specific occupation. The results show that although all occupations have some degree of under-reporting, dry stock farmers were most strongly affected and were inferred to contribute as many cases as dairy farmers to the burden of disease, despite dairy farmer being recorded much more frequently. Rather than discard records with some missingness, we have illustrated how mathematical modelling can be used to leverage information from these partially complete cases. Our finding provides important evidence for reassessing the current minimal use of animal vaccinations in dry stock. Improving the capture of specific farming type in case report forms is an important next step.

scholarly journals An optimization model of a New Zealand dairy farm

2013 ◽  
Vol 96 (4) ◽  
pp. 2147-2160 ◽  
Author(s):  
Graeme J. Doole ◽  
Alvaro J. Romera ◽  
Alfredo A. Adler
Keyword(s):  
New Zealand ◽  
Optimization Model ◽  
Dairy Farm

Retention capacity of biochar-amended New Zealand dairy farm soil for an estrogenic steroid hormone and its primary metabolite

Soil Research ◽  
2010 ◽  
Vol 48 (7) ◽  
pp. 648 ◽  
Author(s):  
Ajit K. Sarmah ◽  
Prakash Srinivasan ◽  
Ronald J. Smernik ◽  
Merilyn Manley-Harris ◽  
Michael Jerry Antal ◽  
...  
Keyword(s):  
New Zealand ◽  
Steroid Hormone ◽  
Dairy Farm ◽  
Equilibrium Concentration ◽  
Resonance Spectroscopy ◽  
Percentage Increase ◽  
Sorptive Capacity ◽  
Retention Capacity ◽  
Sorption Ability ◽  
13C Nuclear Magnetic Resonance

We examined the retention ability of a New Zealand dairy farm soil amended with 3 types of biochar produced from a variety of feedstocks for a steroid hormone (oestradiol, E2) and its primary transformation product (estrone, E1). Biochars produced from corn cob (CC), pine sawdust (PSD) and green waste (GW) were characterised by scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and solid-state 13C nuclear magnetic resonance spectroscopy. Batch sorption studies were performed on soil amended with each biochar (0.5% and 1% by weight) using a complex solvent extraction scheme, and isotherms were fitted to the Freundlich model. All isotherms were highly non-linear, with N values in the range 0.46–0.83 (E2) and 0.66–0.88 (E1) in soil amended with different percentages of biochars. Overall, addition of all 3 biochars was found to increase the soil sorption affinity for the hormones, with E2 sorption being the highest in the soil amended with 1% PSD biochar. There was no marked difference in hormone sorption ability in the other 2 treatments (soil treated with 1% CC biochar and 1% GW biochar). Overall, the effective distribution coefficient (Kdeff) values for E2 at the lowest equilibrium concentration (Cw 0.5 mg/L) ranged from 35 to 311 L/kg in soil amended with the 3 types of biochar. Addition of 0.5% of PSD biochar resulted in ~560% increase in the Kdeff value for E2, while at 1% addition of PSD biochar, uptake of E2 was nearly 1400% higher than the control. For E1, the percentage increase in Kdeff was comparatively smaller than E2; however, it still ranged from 40 to 280%, and 60 to >320% at addition of 0.5% and 1% PSD biochar, respectively, compared with the control soil. Highest treatment temperature and associated greater surface area, low ash content, higher carbon content, and the abundance of polar functional groups (e.g. –OH, C=O) may explain why the soil amended with PSD biochar exhibited high sorptive capacity for the hormones.

Empowering farmers for increased resilience in uncertain times

2015 ◽  
Vol 55 (7) ◽  
pp. 843 ◽  
Author(s):  
R. Nettle ◽  
M. Ayre ◽  
R. Beilin ◽  
S. Waller ◽  
L. Turner ◽  
...  
Keyword(s):  
Social Sciences ◽  
New Zealand ◽  
Agricultural Research ◽  
Dairy Farm ◽  
Significant Shift ◽  
Research Development ◽  
The Past ◽  
Level Of Activity ◽  
The Social ◽  
Research Designs

As farmers continue to face increasingly uncertain and often rapidly changing conditions related to markets, climate or the policy environment, people involved in agricultural research, development and extension (RD&E) are also challenged to consider how their work can contribute to supporting farmer resilience. Research from the social sciences conducted in the past decade has focussed on adaptability or adaptive capacity as a key attribute for individuals and groups to possess for managing resilience. It is, therefore, timely to ask the following: do current ways of doing and organising RD&E in the dairy sector in New Zealand and Australia contribute to supporting farm adaptability? This paper reports on results from an examination of case studies of challenges to resilience in the dairy sector in Australia and New Zealand (i.e. dairy farm conversion, climate-change adaptation, consent to farm) and the contribution of dairy RD&E in enhancing resilience of farmers, their farms and the broader industry. Drawing on concepts from resilience studies and considering an empowerment perspective, the analysis of these cases suggest that, currently, agricultural RD&E supports adaptability in general, but varies in the strength of its presence and level of activity in the areas known to enhance adaptability. This analysis is used to generate principles for dairy scientists and others in the RD&E system to consider in (1) research designs, (2) engaging different farmers in research and (3) presenting research results differently. This represents a significant shift for the science and advisory communities to move to methods that acknowledge uncertainty and facilitate learning.

The role and value of diverse sward mixtures in dairy farm systems of New Zealand: An exploratory assessment

2017 ◽  
Vol 152 ◽  
pp. 18-26 ◽  
Author(s):  
Alvaro J. Romera ◽  
Graeme J. Doole ◽  
Pierre C. Beukes ◽  
Norman Mason ◽  
Paul L. Mudge
Keyword(s):  
New Zealand ◽  
Dairy Farm ◽  
Farm Systems

Biogas production from anaerobic waste stabilisation ponds treating dairy and piggery wastewater in New Zealand

2007 ◽  
Vol 55 (11) ◽  
pp. 257-264 ◽  
Author(s):  
J.B.K. Park ◽  
R.J. Craggs
Keyword(s):  
New Zealand ◽  
Energy Recovery ◽  
Biogas Production ◽  
Ghg Emissions ◽  
Dairy Farm ◽  
Gas Production ◽  
Dairy Wastewater ◽  
Average Volume ◽  
Piggery Wastewater ◽  
Waste Stabilisation Ponds

New Zealand has over 1000 anaerobic wastewater stabilisation ponds used for the treatment of wastewater from farms and industry. Traditional anaerobic ponds were not designed to optimise anaerobic digestion of wastewater biomass to produce biogas and these uncovered ponds allowed biogas to escape to the atmosphere. This release of biogas not only causes odour problems, but contributes to GHG (greenhouse gas) emissions and is wasteful of energy that could be captured and used. Biogas production from anaerobic stabilisation ponds treating piggery and dairy wastewater was measured using floating 25 m2 HDPE covers on the pond surface. Biogas composition was analysed monthly and gas production was continually monitored. Mean areal biogas (methane) production rates from piggery and dairy anaerobic ponds were 0.78 (0.53) m3/m2/d and 0.03 (0.023) m3/m2/d respectively. Average CH4 content of the piggery and dairy farm biogas were 72.0% and 80.3% respectively. Conversion of the average volume of methane gas that could be captured from the piggery and dairy farm ponds (393.4 m3/d and 40.7 m3/d) to electricity would reduce CO2 equivalent GHG emissions by 5.6 tonnes/d and 0.6 tonnes/d and generate 1,180 kWh/d and 122 kWh/d. These results suggest that anaerobic ponds in New Zealand release considerable amounts of GHG and that there is great potential for energy recovery.

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