copy the linklink copied!Chapter 6. Case Study 1. New Zealand Our Land and Water National Science Challenge

This case study provides a practical example of how digital tools can be used to improve understanding of nutrient sources and their attenuation pathways, and agriculture’s impacts on water quality outcomes and policy options for management of water quality impacts, as part of a complex national innovation initiative.


copy the linklink copied!Context: A new approach to sustainable, productive agriculture in New Zealand

New Zealand’s Our Land and Water National Science Challenge (the Challenge) is a mission-oriented,1 government-funded, research and innovation programme, which aims to “enhance primary sector production and productivity while maintaining and improving our land and water quality for future generations”.2 The Challenge, which commenced in January 2016 and is ongoing, is comprised of three Research Themes3:

  • Greater Value in Global Markets

  • Innovative and Resilient Land and Water Use

  • Collaborative Capacity

The second Research Theme (RT) – Innovative and resilient land and water use – is the primary focus of this case study. The goal of this RT is “to help land managers to grow the profitability and yield of productive land uses within the allowable environmental limits by providing widely applicable science and tools to understand the ‘off-farm’ environmental risks associated with a specific area of land.” This goal is set within the context of New Zealand’s 2014 National Policy Statement – Freshwater Management (NPS-FM), which sets statutory requirements for freshwater bodies and requires Regional Councils to meet these objectives.4 This RT will “evaluate, model and assess land and water resources and the environmental, social, cultural and financial suitability of land use practices. [It] will look at new technologies, concepts and enterprises that enable individual and collective land and water users and regulators to best adapt to market signals, to derive optimal value chains and achieve their primary production targets within community and regulatory limits.”5 Thus, this RT will assist land managers, communities and regulators.

To achieve its goals, the RT is comprised of a number of research programmes (>NZD 1 million investment) and smaller projects (refer to Table 6.1).

The Challenge as a whole envisages a new approach to fostering a primary agriculture sector that is both productive and sustainable; captured in the idea that “having the right enterprise in the right location at the right time will deliver the right outcome for individual property owners and catchment communities”. The Challenge aims to enable New Zealand to “move from considering land use capability (generally driven by production potential and other factors such as off-site environmental impact) to land use suitability where economic, environmental, social and cultural factors are considered together” (Our Land and Water National Science Challenge, 2015, p. iv[1]).

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Table 6.1. Research programmes and projects under the Innovative and Resilient Land and Water Use theme of New Zealand’s Our Land and Water National Science Challenge

Research programme/project


Challenge funding (NZD million)1

Co-funding (NZD million)1

Sources and Flows

To understand the fate, transport and attenuation processes of key contaminants - nitrogen, phosphorus, sediment and microbes - within catchments and from catchments to receiving waters, in order to (i) support more informed decision-making on investment in land use activities; (ii) enable land managers and regulators to identify the critical contaminants that will result in environmental impact from specific land uses and locations, as well as acceptable limits of discharge to enable the most cost-effective and appropriate level of mitigation for their enterprise; and (iii) identify which contaminants have potential headroom2 to allow for increased production within environmental constraints, or where catchment re-design utilising low environmental footprint land use options are required.



Land Use Suitability

To help stakeholders in land use and management evaluate different approaches for sustainable production within the constraints posed by environmental objectives (also expressed as ‘managing within limits’).



Next Generation Systems

To provide a framework to enable critical assessment of transformational land use systems and use science from across the Challenge to address barriers to adoption of new systems. Next generation systems is designed to work with the land-based primary sector in enabling transformative innovation under nutrient limiting conditions.



Assessing the Yield and Load of Contaminants with Stream Order

To determine the load (kg/yr) of catchment contaminants that come from large or small streams, and if excluding livestock from large streams (> 1-m wide, >30-cm deep) in flat catchments used for pastoral grazing would substantially decrease the load of catchment contaminants.



Interoperable Modelling

To develop a modelling system populated with models which draws on national datasets and is implemented in an interoperable modelling framework.

This modelling system will be used nationally for integrated and spatial assessment of economic, production and environmental implications of land use and land use change.



Innovative Agricultural Microbiomes

To provide a better understanding of microbiome structure and environmental function, and the implications for (dairy) farm system productivity and sustainability.



Faecal source tracking

To identify the potential sources of faecal contamination impacting waterways to ensure appropriate and targeted mitigation steps are implemented for appropriate land use and to reduce stakeholder risk.



Cascade of soil erosion to rivers

To test he feasibility of developing physically based equations of soil erosion and sediment transport at the landscape scale.





of New Zealand (PENZ)

The physiographic approach seeks to explain ‘how’ and ‘why’ shallow groundwater and surface water quality varies across different landscapes, even when there are similar land uses or pressures in a catchment. This project provides a map that explains these drivers of water quality across New Zealand.



Benign denitrification

in groundwater

To create a rapid and cost effective technique to measure and map complete benign subsurface denitrification hotspots in New Zealand agricultural catchments.



Measuring Groundwater


To develop and validate a methodology for measuring dissolved neon. This project enables the concentration of excess nitrogen to be derived, allowing for the extent of denitrification in groundwater systems to be quantified.



1. Challenge programmes and projects are supported by approximately NZD 12 million in co-funding from government, industry and the science sector. This table lists Challenge funding and co-funding separately. The proportion funded by Challenge funding versus co-funding varies across programmes and projects. For example, the interoperable modelling programme receives only NZD 0.9 million because it has NZD 2.75 million in co-funding.

2. McDowell et al. (2018, p. 215[2]) define “headroom” as follows: “The receiving environment has headroom when the total delivered load is less than the maximum acceptable load (i.e. the ratio is less than one).”

Sources: NIWA (n.d.[3]), Our Land and Water (2018[4]), Our Land and Water (n.d.[5]).

copy the linklink copied!Use of digital technologies in the Innovative and Resilient Land and Water Use Research theme

The problems

The key goal of the Innovative and resilient land and water use RT is to move to a Land Use Suitability (LUS) framework for New Zealand agriculture. Existing efforts to manage land for (environmental) sustainability are based on land-use capability (LUC) classifications. LUC classification defined as “a systematic arrangement of different kinds of lands according to those properties that determine its capacity for long-term sustained production” (Lynn et al., 2009, p. 8[6]). Data requirements for LUC classification therefore relate to on-site physical and environmental characteristics. In contrast, the Land Use Suitability (LUS) classification which the Challenge aims to produce integrates “information about the economic, environmental, social and cultural consequences of land use choices” (McDowell et al., 2018[2]), and thus requires substantially more, and different, data than was needed previously. Thus, achievement of this Research Theme’s objective requires a number of different information gaps6 to be filled. Key gaps include:

  • Information about natural processes (e.g. nutrient and other contaminant pathways), including their spatial and temporal characteristics.

  • Information about how producers and other land managers respond to incentives (both policy and other incentives).

These information gaps also prevent the targeting of existing policies to take into account local contexts. For example, whereas many researchers note that nutrient or other contaminant loss factors (from agriculture and other sources) vary widely depending on location-specific factors, current implementation of New Zealand’s National Policy Statement of Freshwater Management (2014) applies uniform contaminant loss factors “to all areas of land as there are not the tools or frameworks available to link contaminant losses from different parts of a landscape to different levels of water quality impacts downstream.”7

Further, the existing research landscape is characterised by fragmented and asymmetric information: often, data sets and digital modelling tools are accessible only by the researchers who work with them directly. This leads to duplication, confusion over the role of different models and research efforts, and impedes effective translation of research efforts into change “on the ground” (McDowell et al., 2017[7]). In addition, licensing issues with some of the datasets mean data sharing between researchers could be difficult. Case study participants observed that in a collaborative setting, the researchers can settle for a common minimum data that is accessible to all, but which may not be the most up-to-date dataset.

Digital solutions

The Challenge is making use of a number of digital tools to address the information gaps and asymmetries identified above. In some cases, pre-existing tools are being repurposed to help achieve Challenge objectives; in other cases, Challenge funding is being used to enhance pre-existing tools or build new ones. These tools constitute an important part of Challenge activities, but it is important to recognise that they are being developed and used alongside other (non-digital) activities.

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Table 6.2. Digital tools developed under the Innovative and Resilient Land and Water Use Research theme

Digital tool

Challenge research programme

Brief description of tool

Data used by tool (if applicable)

Status as of September 2018

Benefits of tool

Data collection tools

Land, Air, Water Aeotearoa, Ministry for the Environment

National register of measures, interoperable models and data ecosystem white paper

Metadata standards to facilitate the supply and use of environmental data between Challenge modelling tools and central and regional government repositories

National coverage of point data for land and water quality parameters

The Challenge provides advice and funding to continues this work to ease the handover of Challenge modelling and tools

To steward the Challenge’s modelling and tools and create a legacy beyond the life of the Challenge.

Digital analytical tools


Sources and Flows

Framework provides a conceptual link between contaminant source, transport from land to water via surface and subsurface pathways and attenuation during transport processes. The Framework is placed within a hydrology sub-framework that is applicable to all contaminants. The Framework is agnostic to spatial and temporal scales.

National scale climate, flow and water quality data

The conceptual framework development has been completed. Three contrasting case study catchments have been chosen for testing the robustness and applicability

The biophysical Framework allows linkages to other non-biophysical frameworks and components such as cultural, economic and social. This linkage shall be piloted in one of the case study catchment in 2018-19 to allow the community envisage the perceived values of such linkages.

Because they are not bundled into a tool, the Framework layers could be used and manipulated to suit the stakeholder and end-user needs.


Aligned programme a

A spatial farm tool that maps critical source areas of contaminant losses to water and provides estimates of the cost-effectiveness of measures to mitigate their loss

National scale soil and climate data. Farm management data provided by industry standard model – Overseer (

Released July 2018 (

Enables farmers to estimate the likelihood and cost-benefit of reaching an allocation limit.

National physiographic classification



of New Zealand (PENZ)

The main outputs will be:

1. Classed process-attribute GIS layers that depict the spatial coupling between process signals in water and landscape attribute gradients. GIS layers will include:

  • Hydrological Process-Attribute Layer b

  • Redox Process-Attribute Layer c

  • Physiographic Map (combined hydrological and redox process layers)

2. A web-based interface for farmers and industryd

National scale landscape attribute data (from pre-existing GIS layer for geology, soil, topography, climate data, land cover, water flow and quality data e

Finer scale soil mapping, LiDAR, radiometric imagery data to augment national scale data e

A physiographic classification (Physiographic Environments of New Zealand) is currently being created for 7 regions in NZ f. The web-based interface for farmers and industry to access physiographic science is initially being developed for NZ’s Southland region as part of a government grant. Development and design of the web-based interface is being guided by farmers, industry groups and extension staff.

Provides an opportunity to target and implement mitigations that are environmentally- and cost-effective by explaining, at the process level, ‘how’ and ‘why’ water quality and composition vary under similar levels of land use intensity.

Land Use Suitability digital tool

Land Use Suitability

A concept and prototype GIS-based tool for analysing land-water systems. The first application of the concept examines productivity within environmental constraints and produces three indicators:

  • Productive potential: a classification for what a land parcel can do and the value it can return

  • Relative contribution: how much contaminant a land parcel is reaching a site of impact, relative to others, after taking into account attenuation

  • Pressure: whether or not a water body is exceeding the allocated load (as set by an objective)

National scale climate, water flow and quality and land use capability data.

Concept has been published and a prototype tool has been developed and tested in one region (Southland). The tool will be extended nationally in 2019, and augmented with other attributes (e.g. social and cultural).

Provides an objective measure of land use relative to an environmental objective at a land parcel and catchment scale.

Informs policy that seeks to address what environmental objectives can be achieved beyond implementing measures to reduce losses, but sustain, the current (and potentially underperforming) land use.

OVERSEER science and capability

Aligned project under this research theme

Enhancements to NZ’s OVERSEER® nutrient budget model (, the industry standard for estimating N and P losses from different enterprises.

Farmer, consultant, or researcher inputs are augmented by nationally available databases on soil and climate.

Model was first developed in the mid 1990’s and is freely available.

This work will continue to develop new science for incorporation into NZ’s OVERSEER® model. The Challenge is specifically funding work to make Overseer interoperable with other catchment scale models.

River Environment Classification digital stream network layer

Supports Challenge research but is not funded by Challenge

Upgrades existing River Environment Classification digital stream network GIS layer to significantly improve the spatial definition of the network.

Point elevation data and remote sensing information from LiDaR surveys

The first iteration of this new network layer was completed for both North and South Islands and made publicly available in June 2018.

Facilitates development of the NZ Water Model, a sophisticated computer model framework that will enable users to accurately predict how much freshwater is available, where it has come from, and how quickly it moves through New Zealand catchments.

National Catchment-scale Source-Delivery-Attenuation modelling

Used by Sources and Flows but was developed prior to the Challenge

A national scale, scenario-based water quality modelling tool that allows modelling of contaminant (N, P, and sediment) loads from catchments to water bodies.

National scale water quality data from river, farm scale data from a farm scale model OVERSEER

The model has been applied to entire NZ to understand critical knowledge gaps across the country.

Tool allows identifying areas where insufficient information exists in characterising land management and its impact on water quality.

Data management tools

Interoperable modelling framework

Interoperable Modelling

Nationally-recognised modelling platform for assessment of environmental, production and economic implications of land use and land use change. The platform (Deltashell) will be populated with models and national datasets.

Models are to use the best available data from central and regional government (see Land, Air, Water, Aeotearoa)

Programme initiated.

Provides a modelling framework that can be used for a variety of purposes, including regulatory limit setting, land-water management, and contaminant accounting at farm and catchment scales. This will reduce costs, duplication and uncertainties caused by using different models for different purposes, and foster collaboration and a shared understanding of environmental and economic impacts of different land use options.

Digital communication tools and service delivery platforms

Land, Air, Water Aeotearoa

National register of measures

Hub and advisory service that displays and explains the state and trends associated with air, land and water quality data from regional authorities (

All data collected by regional authorities, limited in the first instance to water quality, but to be extended to other domains when data availability and quality allows.

Initiated June 2018. Initial scoping to June 2019 with the intention of it being augmented from July 2019 with a list and location of catchment management groups who have or are using measures to mitigate the effects of land use on water quality, the performance of measures and advice on what measure to use to meet an environmental objective.

Stakeholders (farmers, industry, regulators) will have greater confidence in implementing measures to improve water quality to meet a limit. Advice will emphasise what, where and when to implement measures.

Notes: a. Aligned programmes are those that offer support and significantly advance the Challenge mission. Aligned programmes/projects are identified through the Research Landscape Map and formally documented vis-à-vis their milestones/deliverables and to what key performance indicator they advance.

a. HPAL provides landscape controls over: 1. Water source (where does the water in a stream or aquifer originate from); 2. Recharge mechanism (the broad scale mechanism/process by which water reaches an aquifer or stream); 3. Water pathway (fine scale mechanism/process controlling the pathway water takes – bypass flow, overland flow, lateral drainage and deep drainage).

b. RPAL for soil and aquifer reduction potential controls: 1. Denitrification; 2. The solubility, leachability and mobility of redox sensitive species.

c. The output to produce a web-based interface is funded by The Ministry for Primary Industries through the Sustainable Farming Fund. This is aligned with the OLW PENZ project which delivers the science output while the web-based interface aims to make the science (physiographic map) more accessible to farmers and primary industry groups to inform farm management decisions.

d. Hydrochemistry and water quality data for surface and groundwater (PENZ test set, LAWA); Climate (Temperature, precipitation, ispscape); Hydrology (REC); Soil (Fundamental Soil Layer, S-Map); Geology (QMap, NZLRI); Topography and Elevation (8m Digital elevation model, LiDAR); Land Cover (LCDB4.1); and additional regional datasets (Land use, Radiometric, soil chemistry).

e. Northland, Auckland, Waikato, Bay of Plenty, Manawatu-Wanganui, Canterbury, and Southland.

Source: Authors, based on information supplied by case study participants, Our Land and Water National Science Challenge (2016[8]) and Rissmann et al. (2018[9]).

Table 6.2 provides a description of the main digital tools being developed or enhanced under the Innovative and Resilient Land and Water Use Research Theme, using the classification of digital technologies presented in the project main report (Table 2.1 in the main report). This table includes several tools which are being advanced through co-innovation (Box 6.1) at the same time as the Challenge tools and which support the Challenge research programmes, but which do not receive Challenge funding. This table does not provide an exhaustive list of all digital tools developed using Challenge funding, as the project is ongoing.8

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Box 6.1. Our Land and Water co-innovation approach

A central tenet underpinning the Challenge is that its objectives will not be achieved unless Challenge participants and stakeholders work together collaboratively (Our Land and Water, 2018, p. 4[4]). Recognising that there is insufficient documented evidence of the benefits of collaboration, the Challenge includes a range of specific efforts to measure these benefits and advance understanding of how collaborative processes can be improved.1 The Challenge implements a new way of working, termed “co-innovation”, which replaces the existing “funder-provider” model. Co-innovation is defined as “individual land managers, primary production sectors, iwi,2 communities, policy makers and scientists all working collectively to identify priority issues and create enduring solutions.” (Our Land and Water National Science Challenge, 2016, p. 4[8]).

Co-innovation involves a much closer relationship with stakeholders than existing approaches. The intent is that this closer relationship will produce research that is fit-for-purpose, relevant and will be used and championed within stakeholder networks.

The Challenge defines several different dimensions (and example metrics) of co-innovation:

  • Co-design: Research questions are developed with stakeholders and signed off as relevant. The Challenge maintains a record of co-designing all programmes with stakeholders.

  • Co-development: This generally involves scientists physically co-locating with stakeholders and stakeholders co-investing. Across the wider research landscape we have seen an increase in the frequency of collaboration by 66% (from 1.6 institutes per research programme in 2015 to 2.6 in 2017), while Challenge-funded programmes maintain an average of 5.3 collaborations.

  • Co-production: Investment in and extension of outputs into outcomes is sustained by stakeholders co-authoring Challenge documents. During the first two years of the Challenge, more than 50% of academic outputs were co-authored with stakeholders.

  • Co-innovation: Outcomes are promulgated by stakeholders, for example a close working relationship with science enables a stakeholder to reach sensible water quality limits

The Challenge aims to test the hypothesis that using co-innovation in science can lead to quicker, more robust and enduring outcomes. In particular, it aims to halve the time taken for an idea to be at its maximum level of use from 16 years (Kuehne et al., 2017[10]).

However, some participants noted that because co-innovation is inclusive and deliberative, the process may in fact take longer compared to a situation where researchers develop a solution with little to no input from users and then “push” the solution to users. This raises a question about whether there is a trade-off between designing solutions which are “better” (in the sense of being more robust, enduring or fit-for-purpose) versus “quicker”, and how to measure these different dimensions in order to evaluate and compare different innovation approaches. The Challenge will also be testing this aspect of co-innovation.

1. See in particular work done under the third Challenge Theme—Collaborative Capacity.

2. Iwi is “the focal economic and political unit of the traditional Māori descent and kinship based hierarchy”.

Source:, accessed August 2018.

Managing data and interaction between digital tools: a vision for a data ecosystem

The many and varied research projects under the Challenge as a whole, and within the Innovative and resilient land and water use RT specifically, are producing a “growing diversity, complexity and volume of data” (Medyckyj-Scott et al., 2016[11]). From the start of the Challenge, it was recognised by the Challenge Chief Scientist and Leadership Team that gathering this data into a shared “data ecosystem” is one of the greatest sources of potential value added for the Challenge as a whole. In 2016, a group of experts from the New Zealand public service and the research sector collaborated to produce a “white paper” on the design of this data ecosystem. The data ecosystem is explained as “a system made up of people, practices, values and technologies designed to support particular communities of practice [in which] data is valued as an enduring and managed asset with known quality” (Medyckyj-Scott et al., 2016, p. v[11]) and defined (Medyckyj-Scott et al., 2016, p. 5[11]) to encompass:

  • Policies regarding data management planning, data custodianship and curation, legal frameworks, and the use of externally sourced data;

  • Procedures and processes to execute those policies and manage data;

  • A data governance framework and organisational structures;

  • Engagement with data consumers and stakeholders; and

  • Technology platforms that will support data collection, storage, description, analysis, linking, delivery and curation.

The data ecosystem is proposed to be “supported, enabled and facilitated by a federated infrastructure in which data may be collected from traditional sources and new technologies, curated, published, analysed, modelled, linked, used and reused but accessed through a single point of access, from its authoritative point of origin, with discovery and visualisation tools” (Medyckyj-Scott et al., 2016, p. 21[11]).

Efforts to date have focused on developing a standard for metadata. However, the Challenge recognises that the issue will cost more than it can afford and that the solution must endure beyond the life of the Challenge (due to end in 2024). Therefore, the Challenge has engaged with central government agencies to act as repositories for data and modelling efforts, such that outcomes can be driven from the legacy of Challenge science.

copy the linklink copied!Lessons learned

Lesson 1. Multi-dimensional integration of digital and other tools is needed to ensure efficiency and effectiveness

Interoperability9 is an important consideration when building new digital tools or enhancing existing ones, and has long been identified as a key factor for efficiency and effectiveness. However, this case study demonstrates that more is needed than interoperability to ensure efficiency and effectiveness: digital technologies need to have clear roles with definable “added value” relative to other tools and relative to policy and programme objectives. This is encapsulated in the notion of making digital tools integrated, both with other tools and with other programmes or initiatives than the one under which they are developed. Dimensions of this integration include:

  • clearly articulating how a new tool complements existing tools, including by considering whether a policy or programme objective can be achieved via leveraging an existing tool (potentially with enhancements) versus building a new tool;

  • acknowledging that digital technologies are only one part of a broader solution;

  • acknowledging that multiple digital tools are needed to accomplish complex policy objectives (e.g. models at different timescales, digital platforms to enable different users to use the same data or model for different purposes, etc.);

  • considering potential uses of technologies that are broader than the current programme or initiative, and what design features will help ensure the re-usability of digital tools (in addition to re-use of data).

Case study participants identified two institutional design features that were instrumental in assisting the Challenge to achieve this integration:

  • The co-innovation approach: as outlined in Box 1.1, the Challenge uses a co-innovation approach which actively includes a diverse range of stakeholders, right from the beginning of project design and throughout projects. This enables the relevance of research questions and likely outputs to be tested ‘up front’. It also increases the ability of Challenge participants to identify what type of new tools might be needed (e.g. digital tools or other tools), whether new tools are genuinely additional to existing tools (i.e. because creators and users of existing tools are included in the design process), and how different tools relate to each other.

  • The data ecosystem ‘white paper’: the question of ‘[w]hat are the best data structures for land and water information to achieve the Challenge Mission?’ was actively considered from the outset of the Challenge. This helped ensure that all project proposals, including proposals for new digital tools, actively considered both existing and recommended data structures and existing data tools.10 As part of this process, the data ecosystem team conducted a collaborative workshop in 2015 (i.e. before the formal commencement of the Challenge) about digital tools to ensure stakeholder’s experiences with existing tools, particularly in relation to challenges, were taken into account (Medyckyj-Scott et al., 2016, pp. v, 11[11]).

Lesson 2. Monitoring and modelling should be viewed as complementary

Often, monitoring and modelling happen as two separate streams of work, and modelling is often described as being needed in the context of incomplete information. This implies that modelling is only needed because of data deficiencies; that is, that monitoring and modelling are substitutes.11

In many cases, data gaps are likely to persist: monitoring of all physical variables of interest is unrealistic, despite advances in sensors, Internet of Things devices (e.g. “smart” agricultural machinery) and remote monitoring technologies which enable much broader physical monitoring at lower cost than previously. Therefore, there will still be a need for models to attempt to “bridge” these gaps.

However, even if all necessary physical measurements could be obtained via monitoring, modelling may still be needed for a variety of functions, such as attributing physical impacts to non-physical drivers (particularly to policy drivers, so that policies can be evaluated), and modelling future scenarios to make ex ante policy assessments and improve planning.

Thus, modelling and monitoring should be viewed as complementary: modelling both uses data and allows for analysis in the absence of data.

Lesson 3. Ensure new digital tools do not create new information asymmetries

While the Challenge aims to produce a range of digital tools and information products which address existing information gaps, there is also the need to develop digital tools and effective stakeholder engagement strategies to ensure that production of new knowledge does not inadvertently produce information asymmetries. (This could potentially occur, for example, if only researchers involved in creating new knowledge or tools had access to them. The Challenge acknowledges this risk and addresses it via its co-innovation approach.

Lesson 4. Creation of dynamic, updatable digital tools can lessen the need to “reinvent the wheel” and better match users’ needs

Reflecting the dynamic nature of many factors relevant to land management decisions, there is strong demand for up-to-date information. Previously, many tools were relatively static, making them less useful and prompting periodical attempts to “reinvent the wheel” (to make tools which better suit users’ needs, which may have changed). Therefore, tools that can allow for rapid update of information better match demand for information, and as such are likely to be used more, both now and in the future.

Lesson 5. Embrace different levels of Data Management Maturity to fit different contexts

There are different levels of Data Management Maturity (DMM);12 it may not be necessary to advance all (or any) participants to the highest level of data management in order to achieve programme objectives. Also, it will take time to progressively move through the different levels of DMM. Strategic planning for transitioning through these levels (including planning for different stakeholders—whether individuals or organisations—to move through levels at different speeds) can be helpful for: (i) identifying the current situation (i.e. which participants are at which level), (ii) identifying which level(s) participants eventually need to reach for the programme or policy goal to be achieved, and (iii) improving the overall level of maturity while still allowing for flexibility and not imposing too high transition costs.

It is also important to recognise that moving towards more advanced levels of DMM may require attitudinal change. For example, the Challenge’s Data Ecosystem white paper (pp. 16, 29) identified that “experience shows that one of the major obstacles in the cultural change is the view that data belongs to “me” and that it is not treated as an asset”. The authors concluded that “it is unlikely that maturity in handling data will emerge if in other ways participants lack a strong sense of community.”

Lesson 6. Ensure initiatives generate “additional” benefits by using a mix of old and new technologies

Digital technologies have been in this case study used both to improve and enhance the functionality of existing analytical systems (e.g. upgrading the NZ Water Model), and to provide wholly new tools (e.g. LUS classification and Physiographic Environments of New Zealand GIS layers) that support decision-making process that were not previously possible. This enables the Challenge to avoid duplication and “reinventing the wheel”, while still ensuring that the tools are fit for purpose. This requires a thorough understanding of the existing analytical tools.

Lesson 7. Digital tools can be used to foster collaboration and overcome traditional roadblocks created by conflicting views and values

Development of new digital tools often requires greater collaboration between different individuals and organisations, and across disciplines. Also, there needs to be strong links between new or enhanced tools developed within the initiative (in this case, within the Challenge) and other tools (e.g. NIWA digital stream network layer).

Digital tools are being successfully used to help parties with different interests and incentives to build consensus. For example, the OVERSEER® nutrient model, which is being enhanced under the Challenge and aligned programmes (e.g. to be made spatially explicit by MitAgator), has been developed using co-innovation and can be scrutinised by all interested parties. It functions as an “authoritative point of truth”, but is able to be updated with the latest available science and incorporate innovations (e.g. new data sources from new sensor technologies).

Lesson 8. Digital tools can enable new information-rich policy paradigms rather than simply improving the granularity of existing information-poor paradigms

Many existing approaches to land use planning and managing environmental impacts are fundamentally based on a recognition that there are substantial information gaps and that assumptions are needed to bridge those gaps (Macey, 2013[12]). Land use capability (LUC) planning is one important example of these existing approaches. While the LUC approach provides “an indicator of the productive versatility of land parcels for a range of land uses and identifies key constraints such as erosion” (McDowell et al., 2018[2]), the focus is on determining what a given land parcel is capable of producing. This approach does not explicitly account for spatial linkages or for policy objectives such as objectives relating to downstream receiving water bodies. Because information on aspects such as nutrient transfer pathways and landscape attenuation capacity has been missing, existing watershed management policies tend to be based on LUC assessments and generally apply uniform approaches to different land-use types. While improved data can help these approaches to become more granular and allow for some degree of targeting (e.g. to focus mitigation or remediation efforts on areas where erosion potential is highest), it is difficult to explicitly take into account complex spatial and dynamic relationships within the LUC framework.

Digital tools such as those being explored in the Challenge can enable new approaches such as the land-use suitability (LUS) approach which are able to explicitly account for these complex spatial and dynamic relationships. Such holistic approaches, while still in their infancy, hold out the promise of designing policies which take into account a much greater degree of complexity, including the ability to evaluate synergies and trade-offs between multiple policy objectives.


[6] AgResearch; Landcare Research; GNS Science (ed.) (2009), Land Use Capability Survey Handbook - a New Zealand handbook for the classification of land, (accessed on 7 August 2018).

[13] Geraci, A. (1991), IEEE standard computer dictionary: a compilation of IEEE standard computer glossaries, (accessed on 7 August 2018).

[10] Kuehne, G. et al. (2017), “Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy”, Agricultural Systems, Vol. 156, pp. 115-125,

[12] Macey, G. (2013), “The Architecture of Ignorance”, Utah L. Rev, Vol. 21/1627.

[7] McDowell, R. et al. (2017), Research landscape map for the Our Land and Water National Science Challenge (2nd Edition), (accessed on 17 August 2018).

[2] McDowell, R. et al. (2018), “The land use suitability concept: Introduction and an application of the concept to inform sustainable productivity within environmental constraints”, Ecological Indicators, Vol. 91, pp. 212-219,

[11] Medyckyj-Scott, D. et al. (2016), Our Land and Water National Science Challenge: A Data Ecosystem for Land and Water Data to Achieve the Challenge Mission, AgResearch, Hamilton, New Zealand, (accessed on 7 August 2018).

[3] NIWA (n.d.), Our land and water and NIWA’s role, (accessed on 23 July 2018).

[4] Our Land and Water (2018), Our Land and Water Research Book 2018, (accessed on 7 August 2018).

[5] Our Land and Water (n.d.), Sources and Flows: Managing contaminant pathways & attenuation to create headroom for productive land use, (accessed on 7 August 2018).

[8] Our Land and Water National Science Challenge (2016), Addendum to: Our Land and Water Toitu Te Whenua, Toiora Te Wai National Science Challenge: 2. The Revised Research and Business Plans, (accessed on 7 August 2018).

[1] Our Land and Water National Science Challenge (2015), Our Land and Water - Toitū Te Whenua, Toiora Te Wai National Science Challenge: Revised Research and Business Plans, (accessed on 7 August 2018).

[9] Rissmann, C. et al. (2018), Integrated landscape mapping of water quality controls for farm planning – applying a high resolution physiographic approach to the Waituna Catchment, Southland, Fertilizer and Lime Research Centre, Massey University,


← 1. “Mission-oriented policies can be defined as systemic public policies that draw on frontier knowledge to attain specific goals or “big science deployed to meet big problems”,, accessed June 2018.

← 2., accessed June 2018.

← 3., accessed June 2018.

← 4. Our Land and Water Revised Plan 2015, p.35. The NPS-FM is an overarching national policy for freshwater management, whose objective is “that the overall quality of freshwater within a region is maintained or improved and Regional Councils have to meet its statutory requirements. The NPS-FM links to the National Objectives Framework (NOF) that outlines the water quality objectives that Regional Councils have to meet, along with the proposed Environmental Reporting Bill increasing the demand for enhanced environmental monitoring and reporting.” (p.14)

← 5. Source:, accessed August 2018.

← 6. Refer to Chapter 2 of main report, which presents the conceptual framework for analysis and identifies that information gaps, information asymmetries, transactions costs and misaligned incentives as sources of fundamental problems for agri-environmental policies, which digital technologies can help ameliorate or overcome.

← 7., p.7.

← 8. See also the Challenge Research Landscape Map, available online at:, accessed August 2018. This document details 350 related Challenge-related projects of >NZD 50 000, some of which involve digital tools.

← 9. Interoperability can be defined as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged” (Geraci, 1991[13]).

← 10. Table 3 in Medyckyj-Scott et al. (2016, p. 11[11]) enumerates existing digital tools that the Challenge will interact with.

← 11. In particular, discussions of the use of modelling to support water quality policies for agriculture often centre on the notion that nonpoint sources (including agriculture) are sources for which it is not possible or prohibitively costly to measure and attribute emissions to particular sources (farms).

← 12. Data Management Maturity (DMM) is a concept and framework for analysing institutional capacity to manage and make beneficial use of data assets. The DMM framework assesses data management practices in six key categories that helps organisations benchmark their capabilities, identify strengths and gaps, and leverage their data assets to improve business performance. See (Medyckyj-Scott et al., 2016[11]) and, accessed September 2018.

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Chapter 6. Case Study 1. New Zealand Our Land and Water National Science Challenge