This page provides an introduction to New Zealand dairy systems for researchers working in, or new to, the sector. It brings together two complementary resources: a Dairy Basics section that outlines key features of New Zealand dairy production systems, terminology, and management practices, and a practical guide to getting dairy research right in the New Zealand context. Together, these resources support researchers to understand how dairy systems operate in practice and to design, implement, and communicate research that is feasible, robust, and relevant within seasonal, pasture-based production systems.
The New Zealand dairy industry is built around seasonal, pasture-based systems that operate very differently from the year-round, intensive systems commonly found overseas. If you’re moving here for work or study, you probably need to get up to speed with the key management practices, regulatory requirements, and sector-specific terminology so you feel more confident working in this environment. The following resources are designed to give you a broad, and comprehensive overview of the key issues in New Zealand dairy production, providing you with enough context to seek out more detailed and specialised information relevant to your particular areas of interest.
An overview of how the New Zealand dairy industry is structured along with key industry statistics
The key life stages of dairy cattle as they move through the herd from birth through death
How farm layout, buildings, and physical infrastructure support milk production
Core principles of pasture growth, utilisation, and seasonality in dairy production
The roles of industry bodies, research organisations, and advocacy groups in the dairy sector
An overview of the regulatory and legal frameworks governing New Zealand dairy farming
This guide supports researchers to plan, conduct, and communicate dairy research that is methodologically robust and grounded in the realities of New Zealand’s seasonal, pasture-based production systems. It is structured around six key stages that reflect how high-quality research is developed and delivered in practice:
At each stage, the guide highlights New Zealand-specific system constraints, data sources, regulatory considerations, and key decision points that influence feasibility, validity, and real-world impact, supporting the development of research that is both scientifically defensible and genuinely useful to the dairy sector.
Research in the New Zealand dairy sector is strongly shaped by the need to remain competitive in global markets through maintaining a credible “clean, green” reputation. This places sustained pressure on the industry to demonstrate high standards of animal welfare, low and responsible antimicrobial use, and environmentally sustainable production, all within predominantly pasture-based systems. As a result, research ideas that gain traction tend to align with system efficiency, risk reduction, and social licence rather than short-term production gains alone.
At present, several broad research domains consistently emerge as priority areas:
Because pasture is the primary feed source in most New Zealand dairy systems, research focused on pasture growth, quality, utilisation, and resilience remains central. This includes work on pasture species selection, seasonal feed supply, responses to climate variability, and management strategies that improve pasture use efficiency without increasing environmental risk. Research in this area underpins system modelling, farm benchmarking, and evaluations of environmental and economic performance.
Milk production remains a critical outcome in New Zealand dairy systems, but research emphasis is increasingly placed on how milk yield and milk solids production are achieved within pasture-based and seasonal constraints. Priority questions focus on lactation curve shape, early-lactation performance, persistency, and the relationships between milk production, body condition, fertility, and animal welfare. There is also strong interest in milking system management, including milking routines, equipment performance, hygiene practices, and decision-making that influence milk quality, somatic cell count, and the risk of chemical or antimicrobial residues. Research that examines milk production and quality as part of a whole-system response, rather than maximising yield in isolation, is particularly relevant in the New Zealand context.
Genetic improvement is a major lever for long-term system performance in New Zealand, particularly traits that support efficient conversion of pasture into milk solids. Current research priorities include fertility, survival, feed efficiency, resilience to metabolic stress, and robustness under seasonal calving systems. Increasingly, genetic research is evaluated in terms of whole-system outcomes rather than single-trait gains, recognising that progress must align with pasture-based production and not replicate assumptions from intensive overseas systems.
Reproductive performance remains one of the most persistent constraints on system efficiency in New Zealand dairy production. The national six-week in-calf rate has shown little sustained improvement over the past 20 years, despite extensive research and extension effort. This metric is a major driver of productivity, replacement rates, animal welfare outcomes, and environmental footprint. Current research questions focus not only on biological risk factors, but also on management practices, labour constraints, and behavioural drivers that limit improvement, with particular interest in why known interventions fail to translate into consistent on-farm change.
Animal health research in New Zealand increasingly emphasises prevention, early detection, and system design rather than treatment alone. Maintaining low antimicrobial use while managing endemic disease risk is central to both market access and social licence. Priority areas include disease prevention strategies suited to pasture-based systems, welfare assessment under extensive management, and approaches that reduce reliance on antimicrobials without compromising animal outcomes. Research that integrates health and welfare with labour availability, decision-making, and system constraints is especially valued.
Environmental performance is now inseparable from dairy research in New Zealand. Key research areas include greenhouse gas emissions reduction, nutrient losses to land and water, and system adaptation to regulatory and climate pressures. Research questions that integrate environmental outcomes with productivity, reproduction, and animal welfare are more likely to inform policy and farm-level decision-making than those addressing environmental indicators in isolation.
There is growing recognition that many technical solutions already exist, but adoption and consistent implementation remain limited. As a result, social science research has become increasingly prominent in the dairy sector. Priority topics include farmer decision-making, advisory relationships, trust in evidence, responses to regulation, and engagement with new tools or technologies. Research that helps explain how and why change occurs, or fails to occur, is increasingly central to achieving meaningful and sustained impact.
In the New Zealand dairy context, a strong research question and rationale is one that is explicit about who or what is being studied, what is being compared, and which outcomes matter for system performance, animal welfare, and sustainability. The following section uses the PICOT framework to outline key considerations for writing clear, relevant research questions for the New Zealand dairy industry.
The population element defines who or what the research is about. In New Zealand dairy research, populations generally fall into three categories: animals, farm systems, and personnel.
Animal populations may include:
Farm system populations may include:
Personnel populations may include:
The intervention or exposure describes what differs between populations and what aspect of the system is being examined. In New Zealand dairy research, this is often a management practice, system characteristic, or decision point rather than a tightly controlled experimental treatment. Common types of interventions or exposures vary by research area.
Examples of interventions or exposures include:
Examples of interventions or exposures include:
Examples of interventions or exposures include:
Examples of interventions or exposures include:
Examples of interventions or exposures include:
Examples of interventions or exposures include:
Examples of interventions or exposures include:
Across all research areas, being explicit about the intervention or exposure helps focus the research question on a defined aspect of the system and clarifies what difference is being examined, supporting a clearer rationale and more interpretable findings.
In New Zealand dairy research, the comparator is simply what you are contrasting your population or intervention against. Many dairy studies implicitly assume a comparison without stating it clearly, which can make it difficult to understand what the results actually mean in practice. Being explicit about the comparator forces clarity about what “better”, “worse”, or “different” looks like in a real farm context.
Comparators in New Zealand dairy research commonly take several forms.
Alternative management practices
Comparisons may be made between different ways of managing the same system component, such as:
Performance categories or benchmarks
Populations may be compared based on existing performance differences, for example:
System or structural differences
Comparators may reflect broader system characteristics rather than single practices, such as:
Time-based comparisons
In some cases, the comparison is over time rather than between groups, for example:
Behavioural or engagement differences
For people-focused research, comparators often relate to differences in behaviour or engagement, such as:
Being clear about the comparator helps ensure the research question is asking a question that can be meaningfully answered and interpreted within New Zealand dairy systems.
Outcome measures describe what the research is trying to change, explain, or compare. In New Zealand dairy research, outcomes are most useful when they align with indicators that farmers, veterinarians, industry bodies, and regulators already recognise and use. Outcomes that are disconnected from sector metrics can be difficult to interpret or apply, even if they are biologically interesting.
Common outcome measures vary by research area are:
Examples of outcomes commonly used include:
Examples of outcomes commonly used include:
Examples of outcomes commonly used include:
Examples of outcomes commonly used include:
Examples of outcomes commonly used include:
Examples of outcomes commonly used include:
Examples of outcomes commonly used include:
In many New Zealand dairy studies, it is appropriate to consider more than one outcome, particularly where changes in management are expected to influence production, animal welfare, and environmental performance simultaneously. However, outcomes should be chosen deliberately rather than exhaustively. Selecting outcome measures that are meaningful to the intended audience is especially important when the goal is to support behaviour change. Outcomes that align with how farmers, veterinarians, advisors, or policy-makers assess success are more likely to be understood, trusted, and acted on, increasing the likelihood that research findings translate into real-world change within New Zealand dairy systems.
The time component of a research question clarifies when an outcome is assessed or over what period an exposure or intervention is expected to have an effect. In New Zealand dairy systems, time is rarely neutral. Seasonal calving, pasture growth patterns, and tightly constrained management windows mean that the timing of both exposures and outcomes can strongly influence interpretation.
Many research questions are most meaningful when they are anchored to a specific stage of the dairy production cycle. Common timeframes include the calving period, early lactation, the mating period, mid-lactation, late lactation, or the dry period. For example, questions about mastitis risk, milk production, or animal welfare often focus on early lactation, when metabolic and workload pressures are highest, while reproductive research commonly centres on the mating period and the weeks immediately following.
Other questions are best framed over a defined production season. Seasonal outcomes such as milk solids production per hectare, six-week in-calf rate, antimicrobial use, or environmental performance are typically assessed across a single dairy year, reflecting how success is judged in practice. In these cases, specifying the production season or dairy year helps clarify the scope of inference.
Some research questions involve comparisons across time, such as before and after a management change, across successive seasons, or before and after the introduction of a policy, programme, or regulatory requirement. Explicitly stating the time horizon in these cases helps distinguish short-term responses from longer-term system effects.
A clear research question should be accompanied by a rationale that explains why the question matters and who the research is intended to serve. In the New Zealand dairy context, this means being explicit about the target audience for the research, such as farmers, veterinarians, advisors, industry organisations, or policy-makers, and considering how they are likely to use the information in practice. Research that anticipates how findings could inform decisions, change behaviour, or support existing management processes is more likely to lead to meaningful improvements in system performance, animal welfare, and sustainability. Articulating this link between the research question and its intended use helps ensure that studies are not only scientifically sound, but also positioned to support positive, real-world change within New Zealand dairy systems.
In the New Zealand dairy context, study design and planning is where research ideas are tested against practical realities. This phase involves deciding not only how a study will be designed, but how data will be accessed, how participants will be recruited, how the work will be funded, and whether the project can realistically be delivered within seasonal, pasture-based systems and time-poor farming environments. Many dairy research projects struggle at this stage, not because the question lacks merit, but because access, recruitment, or resourcing issues were underestimated.
In New Zealand dairy research, study design is constrained by seasonal systems, large herd sizes, limited opportunities for individual animal handling, and voluntary participation by commercial farms. Designs that work in housed or year-round systems overseas often do not translate cleanly to pasture-based dairy systems, where animals may only be handled routinely during milking or specific management events.
Common study designs that tend to work in New Zealand dairy research include:
However, study design must also account for the reality that on-farm data recording is often inconsistent or incomplete. Even where data exist, definitions, timing, and accuracy can vary substantially between farms. Designs that rely heavily on farmer-recorded data therefore need to allow time and resources for data cleaning, validation, and, in some cases, supplementary data collection.
Access to animals for sampling or measurement is another key constraint. Veterinarians and technicians often have limited capacity to make additional farm visits outside routine herd health or reproductive work, and extra handling of animals may not be feasible during busy periods. As a result, study designs are more likely to succeed when data collection is aligned with existing management events such as herd testing, routine veterinary visits, calving, pregnancy diagnosis, or drying-off, rather than requiring additional stand-alone visits.
Individual-animal randomised trials are uncommon outside research herds. Many key outcomes of interest, such as six-week in-calf rate, milk quality penalties, antimicrobial use, or emissions intensity, are measured and managed at herd or farm level, even when derived from individual animals. Study designs should therefore reflect where decisions are made and where change is realistically achieved within New Zealand dairy systems.
Mapping roles and resources in New Zealand dairy research is primarily about understanding who controls access to farms, animals, and data, and how much capacity those people and organisations realistically have to support research. Most dairy data are not centrally owned by researchers, and access almost always requires negotiation, consent, and time.
Key people and access pathways
In practice, access to farms is usually mediated through trusted intermediaries rather than direct approaches from researchers. Common gatekeepers include veterinarians and veterinary clinics providing herd health services, farm advisors and consultants involved in reproduction, feeding, or compliance, and industry or extension staff working with discussion groups or focus farms. These individuals often play multiple roles in a project, including recruitment, contextual interpretation of data, and facilitation of on-farm activities.
The availability of these collaborators is constrained during calving, mating, and other peak workload periods. Planning should assume that additional farm visits will be difficult to schedule and that research activities will need to be aligned with existing routine events rather than added on as stand-alone tasks.
Large industry organisations and milk processors are frequently approached to distribute research invitations and may be reluctant to do so due to concerns about farmer survey fatigue. Securing their support often requires a clear articulation of value to farmers or the sector, minimal participant burden, and assurance that results will be reported in aggregate and not used for compliance or auditing purposes.
Smaller-scale recruitment and engagement through veterinarians, advisors, and existing networks is often more effective, even if it limits sample size. Planning should reflect this trade-off explicitly.
Data sources
Many New Zealand dairy studies rely on existing national or commercial datasets. Key sources include:
Even where data exist, they are rarely complete or standardised. Definitions, recording frequency, and accuracy vary between farms and systems. Studies relying on farmer-recorded or commercial data should explicitly plan for time and personnel to clean, validate, and harmonise datasets, and may need to supplement existing records with researcher-collected data to address gaps or inconsistencies.
Time for data preparation should be treated as a core resource requirement rather than an optional extra.
Budgeting for New Zealand dairy research is not just an accounting exercise. It is where study design decisions are tested against reality. Many dairy projects run into trouble because the budget reflects the scientific question but not the true costs of recruitment, data access, travel, and coordination in seasonal, pasture-based systems.
Budgets should be built around the type of study being conducted and the pathways required to deliver it.
Personnel time is almost always the largest cost and the most commonly underestimated.
Typical examples include:
Indicative costs:
Personnel costs should explicitly include time for:
If these tasks are not costed, they will still occur, just unpaid or delayed.
Fieldwork in New Zealand dairy systems is geographically dispersed and travel costs add up quickly.
Common costs include:
Indicative costs:
Studies involving repeated visits or wide geographic coverage should budget conservatively. Travel costs often exceed initial estimates.
Many national and commercial datasets are not free to access and require staff time on both sides.
Typical cost items:
Indicative costs:
Even when no explicit fee is charged, delays in access can create indirect costs through extended project timelines.
Recruitment is a major cost driver in NZ dairy research and should be explicitly budgeted.
Common cost items:
Indicative costs:
Recruitment almost always takes longer and costs more than expected, particularly for farmer-facing studies.
Some dairy studies require physical materials or specialised equipment.
Examples include:
Indicative costs:
Loss, damage, or incomplete data return should be anticipated and costed where relevant.
Data handling costs are often overlooked but essential.
Common items include:
Indicative costs:
Projects involving commercial or sensitive data may require additional security measures.
Budgeting by study type
As a rough guide:
These ranges vary widely, but they provide a reality check against under-scoping.
In New Zealand dairy research, ethics and regulatory approval is rarely a box-ticking exercise. Approval processes shape what data can be collected, who can be approached, how information can be used, and when a project can realistically start. Poorly planned ethics and regulatory pathways are a common cause of delays, missed seasonal windows, and compromised study designs.
Animal ethics approval
Most dairy research involving live animals requires approval from an Animal Ethics Committee (AEC). This applies not only to invasive procedures, but also to activities such as additional handling, sampling, monitoring, or changes to routine management.
Common triggers for animal ethics approval include:
Even where activities align closely with standard practice, committees may still require approval if the activity is being conducted for research purposes. Ethics applications need to clearly justify why the work is necessary, how animal welfare risks are minimised, and how procedures align with accepted standards of care.
AEC review timelines can range from several weeks to several months, particularly if revisions are requested. Studies planned around calving, mating, or drying-off must factor these timelines in early.
Human ethics approval
Human ethics approval is required for most research involving farmers, veterinarians, farm staff, or advisors where identifiable data are collected. This includes:
Human ethics review focuses heavily on informed consent, privacy, data storage, and how results will be reported. In the dairy context, committees are particularly sensitive to:
Consent processes must be clear about what data are being collected, who will have access, how long data will be stored, and how findings will be reported. Ethics committees often require explicit statements that participation is voluntary and that declining will not affect professional relationships.
Data access approvals and agreements
Many New Zealand dairy studies rely on data that are not owned by the researcher. Access typically requires additional approvals beyond formal ethics review.
Common data access requirements include:
Where regulatory or national datasets are involved, additional permissions may be required from government agencies such as Ministry for Primary Industries. These approvals can take significant time and often involve legal or policy review.
Researchers should assume that data access negotiations will run in parallel with ethics approval, not after it.
Alignment with seasonal timelines
One of the most common planning failures in NZ dairy research is underestimating how ethics and regulatory timelines interact with seasonal systems. Delays of even a few weeks can push data collection past critical windows such as calving or mating, effectively compromising the study.
Ethics planning should therefore:
If approvals are not secured before peak workload periods, implementation may need to be deferred by an entire season.
Common pitfalls in dairy research ethics
Common issues that delay or derail dairy research projects include:
• vague descriptions of animal handling or sampling
• insufficient detail on data confidentiality and reporting
• underestimating the sensitivity of commercial farm data
• unclear consent pathways when recruiting through vets or advisors
• assuming industry data access will be straightforward
Addressing these issues explicitly in ethics applications improves approval speed and reduces the likelihood of later restrictions on data use.
In New Zealand dairy research, operationalising the project plan is about making explicit what the project will produce, when those outputs must be ready, and which tasks need to occur to ensure delivery within seasonal constraints. Because many studies are tied to fixed points in the dairy year, deliverables and milestones must be defined carefully to avoid losing critical windows.
Defining deliverables
Deliverables are the tangible outputs that the project produces. In dairy research, these typically fall into several categories.
These enable the project to proceed at all and are often time-critical:
These allow data collection to occur in practice:
These support later phases of the project:
These are often required by funders or industry partners:
Setting milestones in a seasonal system
Milestones are checkpoints that indicate whether the project is on track to produce its deliverables. In NZ dairy research, milestones should be anchored to production events, not just calendar dates.
Effective milestones are typically framed as:
Milestones should function as decision points. If a milestone is missed, the plan should specify whether the study will be delayed, redesigned, or scaled back, rather than assuming work can simply continue unchanged.
Identifying tasks that support delivery
Tasks are the individual pieces of work required to reach milestones and produce deliverables. In dairy research, many tasks are logistical or coordination-based rather than purely technical.
Common task types include:
Tasks that depend on farmer availability, veterinary visits, or animal handling should be identified early and prioritised, as they are often the most difficult to reschedule.
In New Zealand dairy research, funding considerations should shape study scope early, not be treated as an afterthought. Different funders support very different types of work, at different scales, and with different expectations around industry relevance, timelines, and deliverables. A common planning failure is designing a study that fits no funding mechanism particularly well.
MBIE is the primary source of large-scale public science funding in New Zealand. Dairy research is most commonly supported through:
Typical project scale:
Best suited for:
MBIE proposals require a very strong case for national benefit, scientific novelty, and delivery capability. Purely local or incremental dairy studies are unlikely to be competitive.
MPI supports applied research aligned with sector priorities such as biosecurity, animal welfare, productivity, and environmental sustainability.
Typical project scale:
Best suited for:
MPI-funded projects are expected to deliver practical outcomes and often involve close engagement with industry stakeholders.
DairyNZ funds and co-funds a wide range of dairy research, often in partnership with universities, CRIs, or other agencies.
Typical project scale:
Best suited for:
DairyNZ places strong emphasis on industry relevance, feasibility in commercial systems, and clear pathways to uptake.
AGMARDT supports capability development, leadership, and sector-focused initiatives across the food and fibre sector.
Typical project scale:
Best suited for:
AGMARDT funding is particularly relevant for projects that sit at the science–industry interface.
The Nuffield Farming Scholarship supports individuals in the food and fibre sector to investigate big-picture strategic issues through international travel, sector engagement, and applied inquiry. It is not a conventional research grant, but a leadership and capability fellowship that allows scholars to step back from day-to-day work and explore questions relevant to the future of New Zealand agriculture, including dairy systems, sustainability, social licence, and workforce challenges.
Typical support:
Best suited for:
Nuffield funding is most appropriately used to inform problem definition, build networks, and generate strategic insight, rather than to support primary data collection or experimental research.
The Ellett Trust provides grants and scholarships focused on pasture-based agriculture and farm systems.
Typical project scale:
Best suited for:
Ellett Trust funding is often used to de-risk ideas before applying for larger grants.
Universities remain a major source of funding for dairy research, particularly at postgraduate level.
Common mechanisms include:
Typical project scale:
Best suited for:
In New Zealand dairy research, many implementation problems can be avoided through good study design. Clear protocols, realistic timing windows, and appropriate sample size calculations prevent a large proportion of downstream issues. However, even well-designed studies commonly struggle at the implementation stage with the most common being difficulties recruiting sufficient participants and retaining them over time.
Practical strategies for recruitment and retention in NZ dairy research
Recruitment targets should be stress-tested against New Zealand dairy realities before fieldwork begins. Passive recruitment methods such as industry newsletters, email lists, or open calls typically deliver low enrolment and should be treated as awareness tools rather than primary recruitment mechanisms. In practice, studies that meet recruitment targets usually combine early broad outreach with deliberate follow-up through existing professional relationships once response rates fall short.
Timing recruitment around the dairy calendar is critical. Recruitment and survey response rates are consistently lowest during calving and mating, and studies that ignore these periods often underestimate the number of farms required to approach. Where possible, recruitment should occur outside peak workload windows, or expectations should be adjusted to reflect lower participation during these times.
Retention requires active management rather than assumption. Farms are more likely to disengage when participation burden becomes clearer than initially perceived, when farm management or staffing changes, or when seasonal pressure increases. Retention improves when expectations are clearly stated at enrolment, on-farm demands are minimised, and communication is maintained across the study rather than limited to data collection points.
Survey-based studies should assume modest response rates and plan accordingly. Surveys that are short, mobile-friendly, and clearly linked to a tangible outcome for participants (e.g. feedback, benchmarking, summary results) are more likely to be completed. Follow-up reminders should be expected rather than viewed as optional, and non-response should be tracked so its impact on representativeness can be assessed.
Throughout implementation, recruitment numbers, response rates, and attrition should be reviewed regularly rather than at the end of the study. Early evidence that recruitment or retention targets are not being met should trigger a change in approach, not simply an extension of timelines. Where shortfalls persist, they should be documented explicitly so their implications for statistical power and generalisability can be addressed transparently in later phases.
Phase 5 focuses on converting the data collected into interpretable evidence that can meaningfully compare groups, estimate effects, and inform decision-making. In NZ dairy research, analysis must account for clustered data, strong seasonality, and substantial between-farm variability. Analytical choices should reflect both biological relevance and the structure of dairy data, not just statistical convenience.
The following section highlights general analytical approaches for common outcome measures used in dairy research studies.
| Outcome / KPI | Typical unit | Meaningful difference (rule of thumb) | Common comparison | Appropriate analysis |
|---|---|---|---|---|
| Milk yield | kg milk or kg MS per cow | ≈ 0.1–0.2 kg MS/cow/day or 20–30 kg MS/cow/lactation | System, treatment, time period | Linear mixed models with cow and farm as random effects |
| Somatic cell count (SCC) | cells/mL (often log-transformed) | ≈ 20–30% change or movement between SCC bands | Management group, intervention | Linear mixed models on log(SCC) or generalised models |
| Clinical mastitis incidence | cases per 100 cow-years | ≈ 5–10 cases/100 cow-years | Herds, seasons, interventions | Poisson or negative binomial regression with offset |
| Reproductive performance (e.g. 6-week in-calf rate) | percentage | ≈ 3–5 percentage points | Groups or years | Logistic regression or mixed-effects logistic models |
| Calving interval | days | ≈ 7–10 days | Treatments, cohorts | Linear mixed models or survival analysis |
| Culling or mortality rate | percentage or events per time | ≈ 1–2 percentage points | Groups, seasons | Logistic or survival models |
| Body condition score (BCS) | 0–10 or 1–5 scale | ≈ 0.25–0.5 BCS units | Time, treatment, parity | Linear mixed models with repeated measures |
| Lameness prevalence | percentage affected | ≈ 5 percentage points | Management system | Logistic mixed-effects models |
| Antimicrobial use | DDDvet or mg/PCU | ≈ 10–20% change | Farms, time periods | Linear or count models with farm-level clustering |
| Farm-level profitability proxy | $ per kg MS or per cow | Context-dependent; often ≥ $0.20–0.30/kg MS | Systems, years | Linear models with sensitivity analysis |
Statistical significance alone is rarely sufficient in dairy research. Small but statistically significant differences may be irrelevant in practice, while biologically or economically meaningful differences may fail to reach significance in underpowered studies. Meaningful differences should be defined a priori based on production benchmarks, economic thresholds, or animal health relevance, and revisited during interpretation.
Phase 6 focuses on communicating research findings clearly, accurately, and appropriately for different audiences. In New Zealand dairy research, effective communication requires translating complex, context-dependent results into formats that are meaningful for scientists, veterinarians, industry bodies, and policy stakeholders, without overstating certainty or generalisability. Poor communication at this stage can undermine otherwise robust work, particularly when results are selectively framed, over-generalised, or disconnected from the realities of NZ dairy systems.
The table below summarises journals commonly used to publish dairy and agricultural science research relevant to New Zealand. It includes both international and regionally focused outlets, spanning dairy production, animal science, veterinary science, and agricultural systems. Indicative journal rankings are provided to help researchers consider the relative visibility and positioning of different outlets, alongside their disciplinary scope and relevance to pasture-based production systems.
| Journal | Primary scope | Discipline | Typical ranking* |
|---|---|---|---|
| Journal of Dairy Science | Dairy production, nutrition, reproduction, physiology | Dairy science | Q1 |
| Journal of Dairy Research | Dairy production, lactation biology, milk quality | Dairy science | Q2 |
| International Dairy Journal | Dairy processing, milk composition, product science | Dairy / food science | Q1–Q2 |
| Animal | Animal production systems, biology, sustainability | Animal science | Q1 |
| Animals | Animal welfare, behaviour, production | Animal science | Q2 |
| Livestock Science | Livestock management, performance, systems | Animal production | Q1–Q2 |
| Animal Production Science | Applied livestock and pasture-based systems | Agricultural / animal science | Q2 |
| Agricultural Systems | Farming systems, modelling, sustainability | Agricultural systems | Q1 |
| Agriculture, Ecosystems & Environment | Environmental impacts of agriculture | Agricultural / environmental science | Q1 |
| Journal of Agricultural Science | Crop and livestock production science | Agricultural science | Q2 |
| New Zealand Veterinary Journal | Veterinary science, animal health (NZ focus) | Veterinary science | Q3 |
| Preventive Veterinary Medicine | Population health, disease prevention | Veterinary epidemiology | Q1 |
| Australian Veterinary Journal | Applied veterinary research | Veterinary science | Q3 |
| New Zealand Journal of Agricultural Research | Agricultural science with NZ relevance | Agricultural science | Q3 |
Conferences provide an important mechanism for communicating research findings within the New Zealand dairy and agricultural science community. They allow researchers to share results earlier than journal publication, test ideas with peers, and engage directly with veterinarians, advisors, industry organisations, and policy stakeholders. In the NZ context, conferences often play a dual role as both scientific forums and applied extension platforms, particularly for research with system-specific or practice-focused implications.
New Zealand Society of Animal Production Conference
Annual scientific conference; primary NZ outlet for animal production, dairy systems, nutrition, reproduction, and genetics research.
New Zealand Grassland Association Conference
Long-running conference focused on pasture, forage systems, grazing management, and pasture-based livestock production; highly relevant to NZ dairy.
New Zealand Veterinary Association Conference
Major conference for veterinary science and practice; includes dairy cattle health, welfare, and epidemiology streams.
Australasian Dairy Science Symposium
Held every few years, alternates between NZ and Australia; flagship regional conference for dairy science with strong NZ participation.
New Zealand Agricultural and Resource Economics Society Conference
Relevant for dairy systems, farm economics, policy, and resource use studies.
NZBIO Conference
National biosecurity conference; relevant for dairy disease surveillance, biosecurity, and risk management research.
Advancing Animal Care through Shared Learning