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Technological Foresight for
Rural Enterprises and Rural
Lives in New Zealand
Bruce Small,
5 October 2016
Report for AgResearch
CLIENT REPORT NUMBER: RE500/2016/097
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand i
This report has been prepared as an Internal AgResearch Report and is confidential
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Every effort has been made to ensure this Report is accurate. However scientific
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Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand ii
Contents
1. Introduction .............................................................................................................. 1
2. Factors influencing technological development, adoption and impact .................... 3
2.1 Technological co-evolution ............................................................................... 3
2.2 Technological convergence .............................................................................. 4
2.3 Technological integration .................................................................................. 4
2.4 Miniaturization ................................................................................................... 5
2.5 Cost reduction ................................................................................................... 5
2.6 The Law of Accelerating Returns ..................................................................... 6
2.7 Adoption follows sigmoidal pathway ................................................................. 7
2.8 Sustaining or disruptive innovations ................................................................. 8
2.9 Collaboration and trust .................................................................................... 10
2.10 Responsible technological development ........................................................ 10
3. Relevant emerging and future technologies .......................................................... 12
3.1 Enabling technologies ..................................................................................... 12
3.1.1 Universal ultrafast mobile broadband ............................................................. 13
3.1.2 Wireless technologies, sensor networks and the Internet of Things ............. 13
3.1.3 Cloud computing – cheap data storage and information processing ............ 14
3.1.4 Artificial intelligence, the singularity, and super intelligent machines ............ 14
3.1.5 Sustainable energy generation and storage technologies ............................. 17
3.1.6 Nanotechnology and material science ........................................................... 18
3.1.7 Biotechnologies - DNA, GE, MAS, CRISPR/Cas9, synthetic biology ............ 19
3.1.8 Blockchains – digital security ......................................................................... 20
3.2 Emerging technologies, rural enterprises and rural lifestyles ........................ 20
3.2.1 Emerging technologies - new rural products, businesses, and jobs.............. 24
4. Synthetic food ........................................................................................................ 25
4.1 The disruptive and transformative nature of synthetic foods ......................... 26
5. Conclusions ........................................................................................................... 28
6. References ............................................................................................................. 31
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand iii
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 1
1. Introduction
The sustainability and resilience of rural communities in New Zealand in the face of
changing circumstances and conditions is gaining increasing media and academic
attention with trends towards, and prediction of rural decline (Hawke et. al., 2014; Small
et al., 2015, Spoonley, 2016). This issue is a concern for a range of developed countries
(Karcagi Kovats and Katona Kovacs, 2012). New and emerging technologies are an
important driver of global change, offering both opportunities for, and threats to, business,
welfare, and social-ecological sustainability, including rural enterprises and rural
communities (Sustainable Development Solutions Network, 2013).
Some of these technologies will have incremental influences on improving performance
across agricultural supply chains. However, others may potentially be very disruptive to
the current New Zealand agricultural system, rural enterprises and rural lives. Bower and
Christensen (1995) refer to technologies that maintain an historical trajectory of
improvement and provide incremental development as ‘sustaining technologies’. They
define ‘disruptive technologies’ as innovations which offer a different set of attributes than
those desired by traditional product consumers, but which also have an accelerated
trajectory of performance improvement. Newcomers with truly disruptive technologies can
sneak up and topple incumbent organisations and industries, even sending them out of
business. However, disruption caused by new and emerging technologies, besides
potentially toppling an existing regime, also creates opportunities for new enterprises, new
lifestyles and new regimes.
The theory of disruptive technological innovations (Bower & Christensen, 1995;
Christensen, Raynor, & McDonald, 2015) has some elements in common with Kuhn’s
theory of science. Kuhn (1962) makes a distinction between ‘normal science’ that is,
puzzle solving within the current science system, and ‘extraordinary science’ which
challenges the boundaries and fundamentals of the current science system with a new
paradigmatic approach (Kuhn and Hacking, 2012). Normal science is incremental and
sustains and supports the incumbent systems, practices, values and institutions.
Extraordinary science challenges incumbent systems with transformative and
revolutionary insights, concepts, laws, theories, practices, values and institutions.
An emerging theme in academic literature is the disruption caused in the business sector
by digital technologies. For example, Brody & Pureswaran (2015) identify five vectors of
business disruption created by the Internet of Things (IoT): 1) unlocking of excess capacity
of physical assets, 2) creating liquid, transparent marketplaces, 3) radical re-pricing of
credit and risk, 4) improving operational efficiency, and 5) digitally integrating value
chains. Just as digital technologies can disrupt current business practice, they can also
create radical changes in the way people live their lives. Smartphones and social media
applications have radically changed social communication patterns and intensity in the
past ten years. By enabling real-time, always on, communication across the globe, these
technologies increase social network density and connectivity, globalising knowledge,
news, values, and relationships.
The purpose of this paper is to 1) contextualise factors influencing technology
development and adoption, 2) consider some new and emerging technologies and, 3)
foresight some potential implications for agricultural enterprises and rural communities
over the next 20 years. The paper will primarily focus on digital technologies, which are
being used to develop ‘precision agriculture’ and which will impact on rural living and rural
communities, in terms of education, recreation, healthcare, entertainment, socialising,
teleworking, political participation and socio-ecological resilience. A secondary focus will
be in the fields of biotechnology and synthetic foods.
Following Kuhn, Wayland (2015) proposes that, ‘normal foresight’ is required to consider
the incremental or evolutionary developments that new technologies make to an existing
system, while ‘extraordinary foresight’ may be required to consider revolutionary system
transformation resulting from paradigm changing science and technology. Wayland
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 2
claims that epistemological and ontological boundaries are redefined during revolutionary
or disruptive technological system change. Along with a number of philosophers of
technology (e.g., Bunge, 1977; Jonas 1985; Lenk, 1983; Luppicini, 2008; Moor, 2005), I
claim that axiological boundaries may also be redefined by disruptive and/or
transformative technologies (Small, 2011a).
An awareness of the types of technologies, the threats they pose to current agricultural
systems, enterprises, and practices, and the opportunities they might open up for them,
in both business and rural living, will help rural communities to strategically prepare and
enhance their resilience to future change. Whether a technology is incremental or
disruptive and/or truly transformative, in a Kuhnian sense, will have implications for
foresighting resilience approaches for rural communities and agricultural enterprises.
Different approaches may be required for the different levels of technological change.
Thus, at the incremental end of change, mitigation or some adaptation may be sufficient.
As technological change moves from the incremental to the disruptive, increased levels
of adaptation are required. Movement from the disruptive to the transformative level, may
mean adaptive limits are reached, and systemic transformation may be necessary
(Clément & Rivera, 2016).
Mitigation, adaptation or transformation may each require different levels of social
engagement, issue deliberation, collaboration, social learning and the co-evolution of
values, norms, institutions and rules. Transformative technological change requires
extraordinary foresight because this is a future less clear to our vision (i.e., than
incremental technological change), as it involves significant multiple changes that
radically alter the system, creating new norms and rules with different impacts on different
stakeholders.
The increased cognitive complexity associated with system transformation requires
greater mental effort, stakeholder participation and time to learn about available options,
interactions and feedback processes. Transformative technologies will require a
collaborative, transdisciplinary, co-innovation approach to define and understand the
issues within specific contexts and communities, and co-evolve new socio-ecologically
resilient pathways forward, for stakeholders and community members.
Before selecting a range of technologies to discuss, I first consider some general
influences on technological development, adoption and impact.
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 3
2. Factors influencing technological development, adoption
and impact
Individual technologies such as drones, robotics, driverless vehicles, remote sensing,
Internet of Things (IoT), artificial intelligence (AI), virtual reality (VR), augmented reality
and mixed reality (MR), will undoubtedly each play an important role in future rural life and
rural business enterprises. However, perhaps the most significant impacts will be a
consequence of disruption caused by the co-evolution, convergence and integration,
miniaturization, cost reduction, and the adoption of a wide range of similar and related
technologies. An important crosscutting factor behind technological impact, which also
needs to be recognised, is the exponential rate of development that is currently occurring
in science and technology.
Due to the pressing need for the development of a long-term sustainable agricultural
system which respects planetary boundaries (Lal, 2007; Pimental and Sparks, 2000;
Rockström, 2009; Steinfeld, 2006; Vitousek et al., 1986; Vorosmarty et al., 2004),
supports rural communities and market demands, technological development in these
systemic areas will need to take into consideration local, regional, national and
international values and ethics (Sustainable Development and Solutions Network, 2013).
The technologies, and social ecological systems within which they are embedded, will
need to, demonstrably, be morally and socially responsible.
Thus, according to the Sustainable Development and Solution Network “Countries can no
longer pursue national policies independently of global standards. National governments
and multinational companies have responsibilities regarding climate change, biodiversity,
technology transfer, transparency and mutual assistance” (Sustainable Development and
Solutions Network, 2013, p.4). This indicates the need for international collaboration in
addressing the issue of agricultural sustainability. In order to foresight potential
technological impacts on rural living and rural enterprises, technological co-evolution,
convergence, integration, technological acceleration, disruption, collaboration, and moral
responsibility, must be considered holistically.
2.1 Technological co-evolution
Modern technologies do not develop in isolation from one another, rather they co-evolve
or emerge together in an evolving or emergent technological innovation system (Hekkert
et al., 2007; Lee, Olsen & Trimi, 2012). Take for example the smartphone; in order for
the smartphone to become a reality a range of different technologies were required and
developed either in series or in parallel. These included, small superfast processors, high
resolution touch screens, extremely tough gorilla glass, voice recording, voice recognition,
voice synthesis, solid state digital data storage, microscopic digital cameras, applications
and interfaces appropriate to small screens, mobile broadband technologies (3G and 4G),
optical fibre technology, the Internet, cloud based computing, sensors, GPS, and satellite
communications. A related example, documented in academic literature, is the co-
evolution of software, hardware, network systems, web browsers and applications
(D’Hondt, et al., 2002). Similarly, technology co-evolves with industries and institutions
(Nelson, 1993), and socio-cognitive constructs (Grodal et al., 2015). It has also been
proposed that innovation is a product of the co-evolution of science and technology
networks (Murray, 2002).
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2.2 Technological convergence
Nor do modern technologies remain independent of each other in innovative applications
and technological developments designed to address business and social challenges or
enhance human wellbeing and living conditions. Rather, technological co-evolution allows
a space for technologies to converge around real world problems and convenience (Lee,
Olsen & Trimi, 2012; Jenkins, 2006). Thus, in the case of the smartphone, the
technologies mentioned above converge around the need for human communication.
Some of them, such as high resolution touch screens, voice recognition and synthesis,
applications and interfaces, converge around how humans communicate, gather,
process, and distribute information, and how they communicate and interact with
machines. Other technologies, such as, superfast processors, mobile broadband, optical
fibre technology, the Internet, cloud based computing, and satellites converge around
rapid data processing and worldwide distribution of data and information. An important
area of digital convergence is interoperability between applications, operating systems,
network protocols, multiple cloud processing and data storage systems and proprietary
organisational systems and data handling protocols.
There are a range of acronyms scholars have proposed to describe areas of current
technological convergence. These include NBIC - Nanotechnology, Biotechnology,
Information technology, and Cognitive sciences (Roco et al., 2004) and GNR - Genetics,
Nanotechnology and Robotics (Joy, 2000). Technologies, such as the Internet, can
enable innovative convergence to occur, creating new business models. This may occur
through the grouping of services from different organisations under a single umbrella
service, or along value chains, through collaboration between suppliers, manufacturers,
banks, financial services, partner outsourcing firms, transport providers, distribution
companies and consumers (Lee, et al. 2012).
2.3 Technological integration
These co-evolving, converging technologies may be integrated into innovative devices,
systems, platforms, practices, and behaviours which increase human power over nature
and the social world, enable new efficiencies, create new ways of doing things, and
potentially, enhance the human condition. Smartphones integrate a range of the
technologies discussed above enabling humans to virtually collapse space and time,
instantly communicate with anyone (phone, text, social media, video) and anything (e.g.,
IoT) anywhere across the entire planet. We can instantaneously search databases
worldwide for any recorded information, process data, observe distance places, persons
and machines, remotely control machines, record conversations, take dictation, translate
languages, watch and listen to entertainment, and augment reality with data and
information.
In the field of agriculture a range of technologies (e.g., sensors) on different machines or
devices may collect data on moisture, soil fertility, weather conditions, plant health, etc.
wirelessly streaming the data to cloud servers for analysis and prediction of harvest dates,
yield quantities and quality. The cloud may provide feedback and decision-support to the
farmer on his/her smartphone and send pertinent information to other clouds or parts of
the value chain, where data from many different farmers may be collated and processed
into information useful for transportation and marketing logistics, and the management of
financial markets (Buckmaster, 2016; Clifford, 2016).
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In order to implement an agricultural system such as that just described, integration of
information from a wide range of different providers and different industries, using different
cloud systems, different platforms, proprietary software and proprietary sensor
technologies is required. Similarly, a wide range of different actors own or control different
components of the value chain, different information and knowledge sets, and different
parts of the overall systemic process. Therefore, in this agricultural setting, collaboration
is required amongst these actors in order for integration to occur (Buckmaster, 2016). To
secure the collaboration of all necessary actors, integration must create mutual value for
the actors (Lee, Olsen & Trimi, 2012).
2.4 Miniaturization
When it comes to technology “small is beautiful”, particularly in the digital world.
Miniaturization of digital devices offers higher speed, lower cost and increased capacity
(U.S. Congress, Office of Technology Assessment, 1991). Smaller size means devices
occupy less space, can be transported more easy, have less resource and material
requirements, lower energy requirements and can be mass produced in batches (Hsu,
2002). For example, Moore’s Law predicted that the number of transistors on an
integrated circuit doubles every (roughly) 18 months (Moore, 1965). Co-evolution of
miniaturization of various devices means that they can be integrated together into a single
devices. A consequence of this is that a modern smart phone, which fits easily in a
person’s pocket, contains a wide range of sophisticated sensors and functions, and has
many times the processing power and storage capacity that the first computers (such as
ENIAC – made in 1946), which occupied the space of several large warehouses. Indeed,
Hsu (2002) claims that a palm-top computer from 2002 (much larger and slower than
today’s smartphones) had 108 reduction in size and a 108 increase in computational power
over ENIAC.
Miniaturization helps makes technology available where and when it is needed.
Nanotechnology is a relatively new area of science which takes miniaturization to its
extreme, by focussing on the development of technologies at the nanoscale level. Inspired
by biology and the recently discovered DNA structure, the concept of scientific and
technological miniaturization was first elucidated by the physicist Richard Feynman, in
1959, in a presentation to the American Physical Society. Nanotechnology is currently a
major area of rapid technological growth. As well as a chemical synthesis or
manufacturing processes (which nanotechnology is largely based on today) Feynman
also speculated about the possibility of building small devices to make smaller and smaller
devices eventually building devices molecule by molecule or even atom by atom
(Feynman, 1960). The nano-technologies that are currently being developed are likely to
have profound and disruptive effects on society that will spill over into the agricultural
sector (Drexler, 1986; Mulhall, 2002)
2.5 Cost reduction
Over time, particularly as digital technologies miniaturize, not only has performance
become exponentially better, but costs have dramatically reduced in real terms. Kurzweil
(2004) argues that technological cost-effectiveness increases at an exponential rate.
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 6
Solar PV cells are an example of a technology that is reducing exponentially in cost.
Between 2009 and 2014 the cost of PV solar power reduced by 75% (IRENA, 2015).
It took over ten years and cost nearly $US3 billion to sequence the first human genome.
Originally the project was expected to take much longer, however, the development of
new technologies enabled its early completion. Since the project finished in 2003, the
science and technology to sequence genomes has continued to undergo exponential
development. In 2014 a Californian company, Illumina released a state of the art
sequencer, the HiSeq X Ten which it claims can sequence a whole genome, to industry
gold standards, for a cost of US$1000. It is also claimed that the machine can sequence
up to 18,000 genomes a year (Hayden, 2014). Oxford Nanopore Technologies have
recently developed a miniature, handheld DNA sequencing device called the MinION.
Carlson’s Curve is the name given to the exponential (sometime hyperexponential) cost
reduction and performance that is currently occurring in DNA sequencing technology
(Carlson, 2003).
Cost reduction of digital technologies is also following an exponential path Kurzweil
(2004). However, the pace of cost reduction of semiconductors has varied over time
dropping “…at an average rate of 22.5 percent per year over 1975-94 and at roughly
double that pace over 1994-2001, before reverting to an average rate of about 28 percent
over 2001-04” (Aizcorbe, 2006, Section 2). The exponentially decreasing cost of chip
technology means that digitally based sensors, with application in rural enterprises, will
rapidly become very cheap, making them an affordable enhancement for agriculture.
Examples of other technologies which are currently undergoing exponential cost
reductions are, battery technology, digital cameras, electric cars and Lidar (Seba, 2014).
2.6 The Law of Accelerating Returns
Some futurists claim that the rate of growth of scientific knowledge and technological
development has been exponential throughout human history (Toffler, 1971, 1980;
Kurzweil, 2004). Kurzweil calls this the Law of Accelerating Returns. This is a kind of
generalisation of Moore’s Law (which was originally only proposed for the density of
transistors on an integrated circuit) to all technological progress. Supporting this concept,
studies have found “…exponential relationships between performance and time or
equivalently the fractional (or percentage) change per year is constant” (Benson, 2015,
p.1). Nagy et al. (2013) found this to be approximately true for 62 different technologies
for which they conducted an historical analysis.
Kurzweil (2004), who also conducted an historical analysis of technology progress, argues
not only that technological progress is exponential, but further, speculates that progress
during the 21st century will be equal to 20,000 years of progress at today’s (2004) rates.
He claims that, although the empirical evidence shows that technological progress is
actually exponential, our common-sense mind set is one of ‘linear technological progress’,
and that this is why we generally tend to vastly underestimate technological change into
the medium and distant future. Consistent with this analysis, Bill Gates claims “we always
overestimate the change that will occur in the next two years and underestimate the
change that will occur in the next ten” (Gates et al., 1996).
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2.7 Adoption follows sigmoidal pathway
Adoption follows a sigmoidal pathway; exponential growth until market saturation is
approaching, at which point it levels off. Prime examples of exponential adoption rates are
mobile phones, Internet and mobile broadband adoption. World mobile phone adoption in
1985 was 750,000 or 0.02% of the then population of 4.9 billion people, by 1995 mobile
penetration had reached 91 million subscribers or 1.6% of 5.7 billion people. By 2002
mobile phone subscriptions surpassed fixed landlines. By 2005, there were 2.21 billion
mobile phone subscriptions, 33.9% of the 6.52 billion world population. In 2015, there
were 7.1 billion mobile phone subscriptions approaching 95% of the world’s 7.3b
population (ITU, 2015).
In August 1991 the first World Wide Website (i.e. website = a unique host name) was
created by Tim Berners-Lee. By March 2016 there were more than 1 billion websites. In
2013 alone the number of websites grew by one third from 630 million to over 850 million.
Internet use has followed a similar exponential pattern. In 1993 there were 14.2 million
users, by 2000 this number had increased 413 million users, by 2008 there were 1.6 billion
users, and by 2015 approximately 3.2 billion users or 43% of world population (Internet
Live Stats, accessed 2016). These figures suggest that digital technology adoption has
reached the late majority of adopters and that the world is now ‘digitally turned on’ – digital
technologies are no longer the preserve of innovators or early adopters.
Currently the Internet is transforming into the Internet of Things (IoT). Although estimates
vary, as do the definitions of connected devices, estimates for 2012 were 8.7 billion
connected devices (Statista, 2016). In 2015 estimates were 15 billion connected devices,
with projections of up to 50 billion connected devices by 2020 and value estimated at $19
trillion from 2013 to 2022 (Bradley et al., 2015). Despite this incredibly rapid adoption, the
futurist Keven Kelly (Editor of Wired Magazine) claims that currently the Internet is only at
the very beginning of the services that it will offer (Kelly, 2016).
Mobile broadband (broadband access across cellular networks) has become increasingly
popular since the advent of 3G and 4G networks and the smartphone. Faster network
speeds enable a faster and more pleasant user experience for surfing the net or
downloading content on mobile devices. It is estimated that approximate 69% of the
world’s population had 3G coverage in 2015. On a global basis the cost of mobile
broadband decreased by 20-30% between 2013 and 2014. Adoption of mobile broadband
is proceeding even more rapidly than the adoption of mobile phones did. From a beginning
in 2007, by 2015 approximately 47% of the world’s population had a mobile broadband
subscription (ITU, 2015).
This section and the sections above on miniaturization and cost reduction provide
examples of exponential technological acceleration in digital, genetic, solar and energy
fields, in terms of size, power, density, speed and cost. These factors converge and
integrate into the development of new innovations and technologies for accomplishing old
tasks better or for doing new desirable things that were not previously possible. As the
use benefits, convenience, power, and cost of the new technologies converge to
appropriate levels, their adoption starts to accelerate in the typical sigmoidal adoption
curve. Once a product or technology reaches this point of market readiness, as can as
seen above, adoption can be extremely rapid.
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2.8 Sustaining or disruptive innovations
The theory of technological disruption is generally attributed to Bower and Christensen
(1995). They made a distinction between sustaining technologies and disruptive
technologies. Sustaining technologies “tend to maintain a rate of improvements; that is
they give customers something more or better in the attributes they already value” (p.45).
Disruptive technologies “introduce a very different package of attributes from the one
mainstream customers historically value” (p.45). Initially, the new disruptive technology
may be inferior on the historical market valued attributes, or introduce new attributes not
generally desired by existing markets, and only be attractive to niche markets or for new
applications and new markets. Christensen et al. (2015) claim disruptive innovation can
take hold in two different types of markets; niche low-end markets where a low cost good-
enough product satisfies less demanding customers, and new markets where the
innovation is targeted at a market that is not currently catered for. If the new technology
makes very rapid gains in performance, either for the old valued attributes or for new
applications, then the new technology has the potential to undermine and overtake the
old technology, and in some cases, make it obsolete.
A good example of a disruptive technology is digital photography. This technology was
invented by Kodak in 1976, but initially the quality of a digital image was far inferior to film
and did not fit with the performance requirements of Kodak’s main customer base.
Because of this, Kodak did not aggressively pursue the introduction of digital photography.
Nonetheless, the just good enough image, the ability to review images immediately on
camera, and to store and transfer images digitally, was attractive to a niche market.
Overtime, the performance quality of digital photography rapidly increased to the
equivalent of film while also rapidly falling in cost. The consequence was that digital
photography has nearly completely disrupted and overtaken the photography industry to
the point now where film is almost obsolescent. Kodak, because of their focus on their
profitable paper and chemical photography business, were late to the digital photography
area, missed the opportunity to capitalise on their invention, and as digital photography
took over from film, Kodak went bankrupt in 2012 (Diamandis, 5th June, 2016).
“Digital disruption refers to changes enabled by digital technologies that occur at a pace
and magnitude that disrupt established ways of value creation, social interactions, doing
business and more generally our thinking” (Reimer, 2013). Thus, digital technologies will
disrupt both business and lifestyles. In the business world they present both a threat and
an opportunity. The threat to existing businesses is that they may be undermined, out-
competed and have their current business models invalidated. The opportunity is for the
creation of new innovative business models that compete with established organisations
or industries. According to the Global Center for Digital Business Transformation “digital
disruption now has the potential to overturn incumbents and reshape markets faster than
perhaps any force in history” (Bradley et al., 2015).
Digital technologies have already had a major impact on business and business models
in the 21st Century. The largest, fastest growing and highest value companies in the world
are primarily digital companies or enabled by digital technology.
Uber – world’s largest taxi company owns no taxis (founded March 2009), 2016
value - $US62.5b
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Airbnb – world’s largest accommodation supplier owns no real estate (founded
August 2008), 2016 value- $US25.5b
Skype (founded August 2003), Wechat (founded January 2011) – world’s largest
phone companies own no telco infrastructure, value when purchased by Microsoft
in 2011 - $US8.5b
Alibaba – world’s most valuable retailer has no inventory (founded April 1999)
Facebook – world’s most popular media owner creates no content (founded
February 2004), 2016 value -$US212B
SocietyOne – fastest growing banks have no actual money –peer2peer lending
(founded May 2011), value 2016 - $US100m
NetFlix – world’s largest movie house owns no cinemas (founded as a mail order
DVD service in 1998, became a streaming service in 2007) 2016 value -$US32.9b
Apple (founded April 1976, 2016 value - $US535b) and Google (founded January
1996, 2016 value -$US650b ) – largest software vendors don’t write apps
(Sources: www.business2community.com; Wikipedia, 2016)
Note that despite the rapid rise of these companies, enabled by the advent of digital
technologies, they do not necessarily fit the criteria of “disruptive innovations” as
described by the theory of disruptive innovations. In a recent article considering
developments to the theory over the past twenty years, Christensen et al. (2015),
discussed the case of Uber. While noting that Uber is almost always described as
disruptive, they argued that it is a sustaining innovation, in that it did not enter the market
through a niche market or through a new market, and then, as improvements occurred,
capture the traditional taxi market, as prescribed by the theory. Rather, the Uber digital
innovation provided a service to the traditional taxi market from the beginning.
Christensen et al. (2015) classify Netflix as a disruptive innovation, in that the initial mail
order DVD service was not attractive to Bockbuster’s mainstream impulse customers, only
attracting a niche market. However, the transition to a high quality, cheap, on demand,
streaming service met the requirements of Blockbuster’s mainstream customers,
disrupting their business model. Christensen et al. (2015) do, however, note that Uber
may be disruptive to the limousine or “black car” business. This suggests that innovations
can be disruptive with regard to some industries or sectors but not to others.
Nonetheless, it is clear that digital technologies have enabled completely new business
models, in all the above industries, which have revolutionised how their services and
products are marketed and delivered. Despite the dominance of these businesses in their
particular industries, with the exception of Apple, all of these business megaliths began
life after 1996 and most did not start until the 21st century. What this makes clear is that,
once the conditions are right, digital technologies can cause mega-changes to the
business environment in less than a decade. The Global Center for Digital Business
Transformation claims that “the difference between digital disruption and traditional
dynamics comes down to two main factors: the velocity of change and the high stakes
involved” (Bradley et al., 2015). This is a slightly different interpretation to Christensen et
al. (2015), who assert that disruption is a process involving evolution of the product or
service and that this occurs over time and this temporal element means that incumbents
frequently overlook disrupters.
In regard to agriculture, much of the innovative application of digital technologies could
be classed as “sustaining innovations”, that is, digital technologies are primarily being
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 10
used to enhance and improve current agricultural practices and products making them
and current farm systems more efficient and productive. Thus, digital and precision
agriculture may not necessarily be classified as disruptive, in the sense prescribed by the
theory (Christensen et al., 2015). However, digital agriculture will change how farmers
work (farmers as knowledge workers), the type of education and training requirements
they need to operate in a digital environment, where they get their information from, the
amount of human labour required on farm, and how they reach and communicate with
consumers of their products. While digital agriculture may be largely a sustaining
innovation on-farm, it may be disruptive with respect to agricultural value chains, allowing
farmers to by-pass some of the current actors and shorten value chains (Buckmaster,
2016; Whitehead, 2016).
An example of this approach which ties together the use of the Internet, a web-based
computer application, ethical farming, animal welfare, transparency, provenance, and
shortened value chains, is the company Crowd Cow. Crowd Cow uses the crowd funding
concept to enable a group of people to purchase a cow from a local rancher (meeting the
appropriate requirements) by ordering on line the amount and cuts of meat that they want.
Once enough people have lodged an order to purchase a whole cow, it is butchered, the
meat aged, and then delivered to the purchaser, with no supermarket involved (Duggins,
2016). Crowd Cow illustrates how digital technologies may enable new business models
and reshape value chains.
2.9 Collaboration and trust
The agriculture/food supply chain has four main sectors: 1) Input (e.g., seeds, feed,
chemicals and equipment) and production (field crops, horticultural crops, animals, and
seafood), 2) food processing (manufacturing and packaging), 3) distribution and retailing
(wholesalers, supermarkets, and retailers) and, 4) consumption (restaurants, institutional
food services, and households) (Berti and Mulligan, 2015). There are a large variety of
different companies and actors in each of these sectors of the agricultural supply chain.
Different companies compete with different software platforms, different clouds, different
applications and programs along with data and information from a variety of other sources
such as weather, financial markets, benchmarking, and expert knowledge and advice.
This means that in order for digitisation of the agricultural value chain to successfully
occur, inter-operability standards must be developed (between platforms and software) to
enable data integration and companies and individual actors must be prepared to
collaborate and share data and information. Collaboration and information sharing
requires trust between the various participants and trust that the technologies (e.g.,
sensors for data collection, decision-support software, cloud based computing services
etc.) are secure and perform their functions reliably and accurately (Buckmaster, 2016;
Henry, 2016; Whitehead, 2016).
2.10 Responsible technological development
New technologies, by opening up new, previously unachievable possibilities and
behaviours, also create new moral dilemmas regarding the applications that the
technologies could potentially be used for (Jonas, 1986; Lenk, 1983; Luppicini, 2008,
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Moor, 2005; Small, 2011; Small and Fisher, 2005; Small and Jollands, 2006). For
example, in order to enhance resilience of rural communities and enterprises the use and
application of relevant new technologies needs to fit within the moral imperative of
sustainable development (Small, 2007; FAO, 2012). Sophisticated markets and food
retailers are beginning to insist that the products they purchase must be produced in a
sustainable and ethically acceptable fashion (Isenhour, 2012).
Over the past few decades growth in ethical food consumption (e.g., high animal welfare
standards, sustainable environmental practices, fair trade certified, organics, and locally
grown) has increased (Dowler, 2008). New experimental evidence is increasingly
demolishing society’s Cartesian mechanistic norms regarding the denial of internal
experiences and the consequent lack of attribution of moral status to animals (De Waal,
2016). Thus, science is slowly beginning to force a revision of human attitudes to animals
and the manner in which humans use and treat them and our moral relationship with them
(Singer and Mason, 2006). Although still a relatively small percentage of the market,
global fair trade sales outpaced the growth of conventional food, growing by 20% in 2012
(Fairtrade International, 2013). New digital technologies offer the potential for traceability
of products from their place of origin to consumption, giving tools for moral choice to food
consumers (i.e., paddock to plate, field to fork) (IFCITP, nd; Spelitis, 2015, 17th March).
Although traditional customer values such as price, quality, speed and customization
remain essential, customers are now requiring additional product attributes like
experience, emotional fulfilment and public good (Lee, Olsen & Trimi, 2012). As food
safety, sustainability, provenance and animal welfare are rapidly growing consumer
concerns, in technological foresight, with respect to resilient rural enterprises and
communities, there must also be a focus on responsible technological development and
its application to produce ethical products acceptable to markets.
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3. Relevant emerging and future technologies
In the sections below, some existing and emerging technologies and practices, that may
have significant impacts on rural communities, are identified. These technologies may
affect the lifestyles of rural residents and their access to services, rural enterprises and
business practices, future farm systems, and producer to consumer marketing systems.
The co-evolution, convergence, and integration of emerging technologies may have
profound implications for rural businesses and lifestyles, and need to be taken into
account when planning and strategizing for resilient rural communities across the social,
economic, ecological and cultural domains.
Of course, it is difficult, if not impossible to predict the future. Unknown, unforeseeable
conditions and events arise, and they cannot be factored into future projections. However,
a famous quotation from William Gibson suggests a method through which an attempt at
future projection may be made “The future is already here, it’s just not very evenly
distributed” (cited by Rosenberg, 1992). What will later be the future for the majority, is
now largely the province of visionaries, and innovators at the edges of mainstream
business. The people who are currently developing or using new and emerging
technologies in potentially disruptive ways can give us an idea of where the majority’s
future lies. Nonetheless, a degree of caution in this endeavour is required, as Christensen
et al. (2015) remind us that “success is not built into the definition of disruption: Not every
disruptive path leads to a triumph, and not every triumphant newcomer follows a disruptive
path” (p.4).
Before considering new and emerging technologies and the use to which innovators are
putting them, first I draw a distinction between 1) technologies that have a very broad
enabling function for 2) a whole range of other new or emerging technologies more
proximally useful for rural life and rural enterprise.
3.1 Enabling technologies
Consistent with the concepts of co-evolution, convergence and integration is the idea that
some technologies enable other technologies to operate or function. Sometimes they are
referred to as General Purpose Technologies (Lipsey et al., 2005). Once these enabling
technologies are developed and implemented, rapid development and adoption of other
useful dependent technologies occurs, often from diverse technological fields. The
Internet is an example of an enabling technology.
In contrast some emerging technologies have more immediate or proximal effects on rural
enterprises or rural life. Thus, decision support software helps a farmer decide what to do,
social media and business software enhance personal and business communications.
Admittedly, this distinction is somewhat artificial, in that most technologies enable
something else to be done. Thus, while broadband enables VR, AR and MR technologies,
these technologies enable new education, health, recreation, business, and social
activities. The distinction also suggests a linear relationship between the technologies
which will not always be the case as feedback loops will exist. Clearly, broadband
connectivity is a necessary or enabling technology for a large range of emerging digital
technologies with application in agriculture and rural life.
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3.1.1 Universal ultrafast mobile broadband
Currently, the Internet does not serve some rural populations well – their connectivity is
hampered by low reliability, poor quality and low speeds. Good quality broadband
connectivity is essential for digital agriculture. Once rural areas have access to high speed
broadband then use of the Internet of Things and cloud data storage and processing
technologies become practical and useable to rural enterprises and rural residents. This
will enable context relevant measurements through embedded wireless sensors
uploading to the cloud. Once in the cloud, the use of Artificial Intelligence (AI) to
interrogate the voluminous data generated will help model development for use in decision
support software, automation technologies, and producer-value chain-consumer
communications across a range of digital devices (e.g., phone, tablets, notebooks,
desktop PC’s) (Buckmaster, 2016; Clifford, 2106; Henry, 2016).
The use of these digital technologies has the potential to increase efficiency of current
agricultural practices and disrupt agricultural value chains, enabling relatively small
business enterprises to compete both locally and globally through the “creation of reliable,
secure, robust and economically sustainable ‘short’ supply chains” (Berti and Mulligan,
2015, p. 5). High speed broadband also makes technologies such as social media, VR,
AR, MR, tele-health, tele-education, tele-recreation and virtual democracy usable and
convenient for rural populations. These technologies, which help reduce the tyranny of
space and time, will help remove the isolation and inconvenience that currently makes
rural living unattractive to many people.
It is highly probable that the next five to ten years will see the development of high quality,
ubiquitous, world-wide broadband connectivity. The OneWeb project aims to have a
system of over 600 low earth orbit satellites providing universal world-wide broadband
coverage by 2019 (OneWeb, nd). The current standard for fast mobile broadband is 4G.
However, 5G is being tested by IT companies and it is claimed that 5G networks will start
appearing in 2020. 5G will be between 10 to 100 times faster than 4G. Entire movies will
download in a second, such speeds will be fast enough to support data intensive virtual,
augmented, and mixed reality, immersive Internet and autonomous vehicles. It will have
sufficient bandwidth to enable the exponential growth of connected devices to the Internet
(the IoT), wearable devices and smarthomes. The main tenets of 5G development are
extremely low latency, multi-user and multi-stream connections and pervasiveness -
superfast broadband everywhere (GSMA Intelligence, 2014).
3.1.2 Wireless technologies, sensor networks and the Internet of Things
Wireless technologies and wireless sensor networks are also an essential underlying part
of digital agriculture. As noted earlier, the IoT will connect almost every imaginable
machine and device to the Internet, building data collection and intelligence into the
connected devices. With estimates of 50 billion connected device by 2020 and a total
value at stake of $19 trillion from 2013 to 2022, the Iot, or as it is sometimes referred to,
the Internet of Everything (IoE), is poised to revolutionise all aspects of industry and
human life (Bradley et al., 2015). Sensor technology performance is developing rapidly
and cost is declining exponentially. Accurate and reliable sensors will enable
measurement of all manner of farm and environmental data. With smart sensors
embedded in devices on animals, in fields, in soil, in buildings, in machines, in drones,
and in phones, enormous quantities of data will be able to be collected and wirelessly
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transferred to cloud storage and processing systems. Through the use of big data
analytics, AI learning algorithms, and interdisciplinary science models, the data will be
transformed into sophisticated models of agricultural systems and useable information for
timely decision making and decision support by people and business all along the value
chain, from farmer to consumer (Buckmaster, 2016; Clifford, 2016). The IoT is currently
expanding at an exponential rate.
3.1.3 Cloud computing – cheap data storage and information processing
The cloud provides a convenient medium on which to store and process large amounts of
data. The National Institute of Standards and Technology (NIST) defines cloud computing
as “…a model for enabling ubiquitous, convenient, on-demand network access to a
shared pool of configurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned and released with minimal
management effort or service provider interaction” (Mell and Grance, 2011).
The cloud provides Software as a Service (SaaS) to consumers. This means that the
cloud provides network infrastructure and software that consumers can access and use
without needing to own themselves. This is akin to the concept of a computing utility
service provider. Cloud computing can also provide Platform as a Service (PaaS) and
Infrastructure as a Service (IaaS). There are four main models of cloud deployment:
Private Cloud (owned and managed by a private company e,g., John Deere, Monsanto),
Community Cloud (setup and managed for exclusive use by a specific community of
consumers), Public Cloud (available for public use provided by business, government, or
universities e.g., Dropbox, Amazon Web Services and Elastic Compute Cloud, Google
App Engine, Microsoft Azure) and, Hybrid Cloud (multiple, standardized, inter-operable
clouds enabling portability of data and applications) (Mell and Grance, 2011).
The advantages of cloud computing was listed by Zang et al. (2010) as: no up-front
investment by the consumer, lower operating costs, highly scalable, easy access by
multiple devices, and the reduction of business risks and maintenance expenses. An issue
with cloud computing is concern about the security of data and information. Essentially,
this is a question of consumer trust in the security systems of the infrastructure, platform,
and application service providers.
Cloud technology and services are developing very rapidly and their uptake and value is
currently in hypergrowth. There is a range of estimates and projections of the value of
cloud computing. Forrester believes that business spending on cloud computing will
expand from $US72 billion in 2014 to $US191 billion by 2019 (Cited in Pardo et al, 2016).
International Data Corporation (IDC) projects that the market in 2017 will be $US107
billion, twice the value it was in 2013 (Cited in Pardo et al, 2016).
3.1.4 Artificial intelligence, the singularity, and super intelligent machines
Artificial intelligence (AI) can be defined as the science of making computers do things
that require intelligence when done by humans. AI is based on the conjecture that
“…every aspect of learning or any other feature of intelligence can in principle be so
precisely described that a machine can be made to simulate it” (McCarthy, Minsky,
Rochester, & Shannon, 1955, p. 1). Turing (1950) was one of the first to address the
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question, “Can machines think?” by ‘machines’ Turing was referring to digital computers
– not the ones available in 1950, but rather whether it would be possible for any digital
computer to ever think. To answer this question Turing proposed the “imitation game” now
referred to as the ‘Turing test’. Stimulated by Turing’s ideas, the project to create AI has
since taken many paths, had numerous setbacks, but since the beginning of the 21st
century has experienced exponential growth and success. Many of the different paths and
approaches to AI have contributed knowledge or solved specific types of AI problems, but
even in combination, none has so far been able to produce a computer with the general
intelligence capability of humans (Kelly, 2014).
Nonetheless, striking progress has been made. Machine learning, the convergence of
parallel processing, big data sets and learning algorithms, to find patterns and meaning
in unstructured data, is having significant success. Major digital technology companies,
such as Microsoft, Amazon, Google, Facebook, Twitter, Pinterest, Yahoo, Intel and
Dropbox are investing huge amounts of money into creating AI. More than $US17 billion
was invested in AI between 2009 and 2014. Neural networks and advanced statistical
techniques (referred to as Deep Learning) are achieving feats once believed to be science
fiction. IBM’s supercomputer, Deep Blue, beat Kasparov, the world champion chess
player at the time, in 1997. Watson, another IBM supercomputer defeated the two world
champion humans at the game of Jeopardy in 2011 (Kelly, 2014). Just recently AlphaGo,
utilising Google’s Deep Mind AI, beat the world champion human at the more complex,
less structured game of Go, not by being programmed with the rules of Go, but rather by
playing games against humans and learning to play as a human would (Metz, 2016).
The concept of ultraintelligent AI was first suggested by Good (1965), who proposed that
once computers surpassed human level intelligence their ability to create smarter
computers would surpass humans. Good claimed there would then be an exponential
explosion in machine intelligence as the computers designed better computers and these
computers designed even better computers, leading to what he described as
ultraintelligent computers. This process is referred to as ‘recursive self-improvement’. This
concept of the exponential explosion of machine intelligence to superintelligence is
frequently referred to as ‘the singularity’ a term coined by Vinge (1993). Bostrom (2014,
ch. 2) defines superintelligence as “any intellect that greatly exceeds the cognitive
performance of humans in virtually all domains of interest”.
Based on Moore’s Law, Kurzweil (1999, 2005) predicts that desktop computers will reach
human level general intelligence by 2029 and that the singularity will occur in 2045. A
survey of four groups of ICT and AI experts (n=550) found the median estimate of
respondents was for a one in two chance that high level machine intelligence will be
developed around 2040-2050, rising to a nine in ten chance by 2075 (Müller and Bostrom,
2014). A number of very famous scientists, technologists and philosophers are so
convinced by the possibility of superintelligent machines that they have warned the world
about their potential existential threat to humanity (Bostrom, 2014; Hawking et al., 2014;
Joy, 2000). For the purpose of this paper it is not necessary to speculate whether
superintelligent machines will possess consciousness or sentience, as sentience is not
necessary for machine learning and intelligent or even superintelligent understanding of
a system.
Computers are already capable of many spectacular intelligent activities such as
language translation which requires natural language processing (knowledge of at least
two languages), reasoning to following the content or argument, knowledge to know what
is being talked about and social smarts in order to understand the author or speaker’s
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intent. Smartphones already have ‘somewhat’ intelligent personal assistant programs that
can respond to voice commands and answer questions e.g., Apple’s Siri, Microsoft’s
Cortana, Google Now, and Amazon’s Echo. Such systems continue to grow in both power
and usefulness to humans. Self-driving cars are another example and current application
of AI.
As AI converges with progress in robotics, cloud computing and precision
manufacturing, tipping points will arise at which significant technological
changes are likely to occur very quickly. Crucially, advances in robot vision
and hearing, combined with AI, are allowing robots to better perceive their
environments. This could lead to an explosion of intelligent robot
applications — including those in which robots will work closely with
humans. (Nature editorial, 2016, p.413)
Continued progress in AI is dependent upon increasing performance from both hardware
and software. Although some question how much longer Moore’s Law can continue to be
true, computational power is still increasing rapidly and developing technologies such as
multifunctional chip circuits, graphene based microchips and quantum computers
potentially offer vast improvements in hardware performance. Regarding the software,
learning algorithms are continuing to accelerate in power and performance (Kurzweil,
2005; Bostrom, 2014). AI is developing at an exponential rate, machines are becoming
smarter and capable of doing more human like activities and this fact will have a very
large impact on jobs and employment including rural enterprises and rural lifestyles.
Wireless sensor networks will provide mega quantities of data, which in combination with
information from other sources, scientific models and the learning algorithms of cloud
based AI, will be able to ‘understand and model’ the data, transforming it into useful
information, helping farming enterprises with decision support and farm automation.
There are many major businesses, as well as innovative start-ups, developing farm
management technologies which integrate remote or embedded sensors with cloud
computing and artificial intelligence to provide tools for improving farmer decision-making
and increasing farm efficiency. Amongst others, this includes: The Climate Corporation,
John Deere, Monsanto, Dupont, NuFarm, WinField, SST Software, Mapshots, Raven,
Trimble, Ag Gateway, Agsolver, Ag Link, Delta Agribusiness, Precision Ag, Hortus
Technical Services, Conservis and Ag Leader (Buckmaster, 2016; Clifford, 2016; Pawsey,
2016). It seems highly likely, given this drive towards digital agriculture by these major
players and innovators combined with the need to increase the efficiency and
sustainability of agriculture, that it is only a matter of time before these digital technologies
are adopted across the agricultural sector.
AI, cloud computing, sensor technologies, the IoT and ubiquitous broadband will also
enable new forms of education, recreation, collaboration, and socialisation, especially in
combination with virtual, augmented and mixed reality technologies. Remote healthcare
will be facilitated by these powerful communication mediums with AI helping diagnose and
advise treatment approaches and/or notifying medical personnel when needed. Virtual
reality, AR and MR will enable innovative remote education services which will be
augmented by AI teacher bots providing individualised learning programs for students,
Digital technology will provide new mechanisms for political engagement, participation,
deliberation and governance. These technologies will open new opportunities for rural
work and rural lifestyles, enabling remotely located people.to participate and interact with
people or machines anywhere on the planet in real time.
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3.1.5 Sustainable energy generation and storage technologies
Fossil based fuels are a significant cause of global climate change necessitating the need
for a massive move to renewable and sustainable energy generation to keep atmospheric
CO2 within a safe operating space for humanity (Rockström et al., 2009). It is clear now
that solar photovoltaics (PV) offers the potential to largely solve humanities energy
requirements renewably and sustainably. Crystalline silicon PV cells have been declining
exponentially in cost per unit of energy generated. In 1976 the cost was $US76 per watt.
By 2000 it was $US5 per watt. In 2015 the cost had reduced to $US0.30. PV electricity
generation is now commercially viable; it has already reached cost parity with grid
electricity in 40 countries and is projected to reach grid parity in over 50% of nations by
2017 (Kalkman et al., 2015). As the cost of crystalline silicon PV has declined
exponentially, adoption has increased exponentially since 2007, doubling every year
amounting to a tenfold increase in 7 years (Green, 2014). Currently, over 90% of installed
PV capacity is crystalline silicon PV (Kalkman et al., 2015).
However, there are a large number of new PV technologies currently being developed
and tested in laboratories around the world that may revolutionise solar energy production,
making it even more efficient, affordable and practical. Six of the most promising and
potentially disruptive (to crystalline silicon PV) new PV technologies are: 1) cadmium
telluride thin film, 2) Copper indium gallium selenide (CIGS) thin film, 3) concentrated
photovoltaics (CPV) and multi-junction solar cells, 4) organic photovoltaics (OPV), 5)
quantum dots photovoltaics, 6) Perovskite PV and, 7) graphene PV (Kalkman et al., 2015).
Current estimates are that solar PV could supply a quarter of total global energy capacity
by 2035 (Green, 2014).
Because of solar PV’s exponential technological development and exponential cost
reduction, Kurzweil considers that the technology will be truly disruptive, that over 50% of
the world’s electric generation capacity will be provided by solar by 2030 (cited in Green,
2014). Seba (2014) is even more optimistic, projecting that by 2030, oil, coal, nuclear,
natural gas, electric utilities and conventional fossil fuel cars will all be obsolete, that nearly
all new cars manufactured in 2030 will be electric and self-driving and nearly all electric
generation will be solar.
Battery technology, the ability to store energy for later use, has been a major impediment
to the development of sustainable and renewable energy generation through solar and
wind technologies. Solar only provides power during daylight and wind generation can be
erratic. Energy dense batteries could help facilitate the use of solar and wind generation
by storing power in battery banks in the grid or by storing power at people’s homes, for
use when wind or sun are not available (Seba, 2014).
Battery technology is a major focus of research with a large number of different and new
and potentially more efficient battery technologies being developed and tested
(ScienceDaily, 2016). The need for better batteries to power electric cars has driven
development in battery technology with the spin-off being that it is now possible to make
affordable battery storage units for homes and for grid energy storage. Tesla is currently
marketing the Tesla Powerpack (Tesla, 2016a) which is a commercial and utility energy
storage battery and the Tesla Powerwall (Tesla, 2016b) which is a home battery. The
Powerwall makes off the grid living for rural residents feasible and economic. It can also
be used with a grid connection to draw energy in low demand periods when power is
cheaper, to be used during peak demand periods.
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When integrated together, solar PV and new battery technologies are potentially
disruptive to the centralised grid based electricity generation system. Soon it will be
cheaper for houses to generate and store their own electricity than to receive power from
the grid, although PV and batteries could also be used to complement and enhance grid
based energy services, feeding power back into the grid at peak demand. Essentially, the
future of electricity generation will be distributed. Distributed generation will make
electricity use more efficient (less loss of power in transmission) enhancing an energy
system’s resilience to catastrophic failure (e.g., Chernobyl), shocks of nature (e.g.,
Fukushima) and acts of terror. Rural enterprises and residents need no longer rely on the
grid, thus enhancing their resilience to nature’s threats. However, if connected to the grid,
solar PV, wind generation, and batteries may enable the creation of new rural enterprises,
such as energy farming.
3.1.6 Nanotechnology and material science
As mentioned in the section on miniaturisation, the field of nanotechnology is undergoing
rapid development and generating a wide range of new materials with exotic properties
and a range of new machines and devices. For example, in the field of solar PV, both
quantum dot technology and graphene technology are nanotech based. Nanotechnology
is finding applications in fields as diverse as energy generation and storage, computing,
communications, cosmetics, new foods, food safety, medicine, environmental science,
transportation, sensors of all kinds, material science, and more efficient and greener
industrial processes (National Nanotechnology Initiative, nd).
A range of applications for nanotechnologies in agriculture have been proposed. These
include, 1) nanopesticides and nanofertilisers to improve productivity, 2) nanozeolites,
hydrogels and nanoclay to improve soil quality, 3) nanomaterials (SiO2, TiO2 and carbon
nanotubes) to stimulate plant growth and, 4) wireless nanosensors to provide smart
monitoring (Fraceto et al., 2016). Wireless nanosensors are particularly relevant to digital
agriculture, not only on the farm but all along the agricultural value chain. On-farm and in
the field nanosensors can be used to control quality, monitor biosecurity and biodiversity,
measure soil parameters such as pH, nutrients, residual pesticides in soils and crops, soil
temperate and humidity, detect pathogens and pests, predict nitrogen uptake, and
manage irrigation (Bellingham, 2011).
Nanosensors will help farmers manage their farms with precise knowledge and control,
minimising inputs, maximising productivity, and reducing environmental impacts and
waste (Fraceto et al., 2016). Nanosensors have the potential to play a pivotal role in
making agriculture sustainable. Post-farm nanosensors will play a role in transportation,
food processing, packaging, and distribution (Scognamiglio et al., 2014). Through their
ability to detect a wide range of target molecules, nanosensors will play an important role
ensuring “food quality, safety, freshness, authenticity, and traceability along the entire
food supply chain” (Fraceto et al., 2016, p. 3).
In the most visionary and utopian projections, it is claimed that nanotechnology will herald
an age of plenty sometimes referred to as the post-scarcity economy; small, even home-
based, nanofactories or molecular assemblers will produce any item any one wants,
building them molecule by molecule (Dexler, 1986). Although nanofactories and molecular
assemblers do not as yet exist, nonetheless they may be important factors to consider in
a more distal future.
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3.1.7 Biotechnologies - DNA, GE, MAS, CRISPR/Cas9, synthetic biology
New and emerging biotechnologies include genomic sequencing (discussed earlier with
regard to the exponential nature of technological development), marker assisted selection
(MAS), genetic engineering (GE), gene editing (CRIPR/Cas9) and synthetic biology.
Marker assisted selection allows the controlled improvement of desired phenotypic
attributes (and does not face the same public resistance as GE). GE is a controversial
technology which raises numerous ethical questions (Small, 2011a,b) and which the
public is currently still wary of (Small, 2011b; Small et al., 2015). However, it has significant
scientific support and has the potential to rapidly produce food plants which can survive
under much harsher environmental conditions than they currently can. Some scientists
even go so far as to claim that it will be essential to meet future food requirements, given
expected population increase, degrading global soil conditions and changing climate
(Borlaug, 1997; Fedoroff et al., 2010).
Apart from its role in food production, GE has the potential to open up new agriculturally
based bio-industries producing such things as pharmaceuticals (Giddings et al., 2000),
vaccines in plants (Levi, 2000) and animal products (Chowdhury and Bagasra, 2007), and
new materials (Scheller, et al., 2001). Genetically engineered plants can also be used for
phytoremediation of polluted soils (Cherian & Oliveira, 2005). Some have suggested that
genetically engineered trees may be able to help sequester greater amounts of carbon
from the atmosphere – helping to reduce a major cause of climate change and global
warming (Jansson, Wullschleger, Kalluri, & Tuskan, 2010).
The gene editing technology CRISPR/Cas9 was developed in 2012 (Jinek et al., 2012). It
is a simple and fast way to make a range of very specific changes to the genome and
epigenome of any plant or animal in order to conduct research to discover genomic and
epigenetic functions and relationships and to produce desired phenotypic changes in
organisms. Since its discovery this technology has been rapidly adopted as a key tool for
use by molecular biologist all over the world. In principle, anything that can be done with
GE can be done with CRIsPR/CAS9 Technology. It is considered to have enormous
number of potential medical applications (Ledford, 2016). CRISPR has enabled the
development of a technology called “gene drive”. A gene drive accelerates the spread of
a modified gene in an organism through an entire population. It is proposed that this
technology could be used to modified mosquito genes to control the spread of diseases
such as malaria and dengue fever (WYSS, nd). Gene drives are also proposed as a
potential technology to help eliminate unwanted invasive species, which in New Zealand,
might include possums, stoats, rats, mice, rabbits and ferrets.
Gene editing is rapidly making its way into agricultural research where it is being tested
for a range of applications such as increasing crop resistance to pests, making crops
drought resistant, creating disease resistant livestock and hornless cattle (Ledford, 2016).
It has been proposed that CRISPR enabled gene drive technology could reverse pesticide
resistance in insects and herbicide resistance in plants, and modify or destroy plant pests
and invasive species (WYSS, nd). Thus, the technology has potential applications in
biosecurity and preservation of biodiversity. However, concerns have been expressed
about gene editing and there is a question as to how well this technology will be accepted
by the public. It is also unclear what the institutional rules that govern its application will
be. Nonetheless, this technology may come to play an important role in the future of
agriculture.
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Synthetic biology is about applying engineering principles to molecular biology to create
novel artificial biological pathways, organisms or devices, which do not exist in nature, in
order to produce desired products. A large range of potential applications are proposed
for synthetic biology such as the creation of industrial enzymes, new materials production
(perhaps in combination with nanotechnology), designer proteins, and even synthetic life
(Chopra and Kamma, 2006; Venter, 2015). Synthetic genetic organisms could be used to
produce fuel to help replace petroleum with bioethanol, butanol and other such products
(Sticklen, 2008). An agricultural application of synthetic biology might be the development
of environmental biosensors for use in digital agriculture. Synthetic biology raises the
same kinds of ethical and biosecurity concerns as GE and gene editing.
3.1.8 Blockchains – digital security
Blockchain technology is touted as having the potential to revolutionise banking and
financial systems by reducing the cost and complexity of transactions and improving
transparency and regulation. However, its potential uses extend far beyond the financial
sector. The Harvard Business Review defined blockchain as “a vast, global distributed
ledger or database running on millions of devices and open to anyone, where not just
information but anything of value – money, titles, deeds, music, art, scientific discoveries,
intellectual property, and even votes – can be moved and stored securely and privately
(Tapscott and Tapscott, 2016, Para 2).
Blockchain will help to manage and secure the Internet of Things, the devices connected
to it and the data they produce. Blockchain enables trustless transaction – transactions
which ensure integrity and trust between strangers, thus opening up new mechanism for
trade over the Internet. Transactions can be securely made between two parties without
the need for a financial institution. Blockchain is the technology that underlies
cryptocurrencies such as Bitcoin (Nakamoto, 2008). For the agricultural producer
blockchain enables a secure and transparent means of trading with consumers
eliminating financial institutions and shortcutting the supply chain. Further developments
and applications in blockchain technology may be relevant to the future of agriculture and
agricultural value chains.
3.2 Emerging technologies, rural enterprises and rural lifestyles
Below, I list a range of emerging technologies, developed already in proof of concept or
early commercial versions, with potential implications for rural enterprises, livelihoods and
lifestyles. The implications of these technologies are not fully developed below, and
indeed they might not be fully foreseeable yet. Rather, brief comments are made about
some potential applications or effects of these technologies.
SmartPhone/SmartWatch/SmartGlasses, augmented reality (AR) devices –are
the devices which interface between humans and the Internet and the Internet of
Things. Wireless sensor networks on farms will transmit information to the
farmer’s phone. Digital agriculture will be accessible and manageable by mobile
devices such as smartphones and tablets (Barcelo-Ordinas et al., 2013) or by AR
devices such as Microsoft’s HoloLens.
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 21
Apps – a wide range of applications will help rural enterprise and rural residents
in their lives and businesses. These include, communication and networking apps,
farm systems apps, animal data, welfare and traceability records, economic and
environmental performance analysis, market information and trading apps,
biodiversity apps, biosecurity apps, and weather warning apps, (Barcelo-Ordinas
et al., 2013). Rural living may be enhanced by social media apps, health and
wellbeing apps, teaching and learning apps and game and entertainment apps.
SmartHouse - the integration of digital devices (IoT) into the home allowing
automation of a range of processes controlling the home environment, allowing
remote access by the owners, and interfacing the house with service and goods
providers. Examples include smart fridges and pantries which are aware of
contents and automatically order more when supplies are getting low, and robots
that can do housework, prepare meals, and take care of elderly, disabled or
infirmed persons, and look after, entertain and educate children.
Smartfarm – a similar concept to the Smarthouse, but for the whole farm system
(Henry, 2016). Smartfarms, enabled by wireless sensors, cloud based AI, digital
devices (smartphones, tablets, desktops, etc.) and robotics, will initially see
decision support systems to assist famers and, longer term, the gradual
automisation of various aspects of farm systems. Data collected by a wide range
of on farm wireless sensor will be sent to cloud based AI which will analyse the
data and provide decision support to the farmer through a software interface on a
mobile device (ICT international, nd). Robots will gradually improve in efficiency
and use and, in the longer term, become cost effective as human labour
replacement on farm. Farmer health, safety and security will be enhanced by an
AI monitoring his/her whereabouts and providing information, such as alerting the
farmer of visitors, or notifying emergency services if something untoward happens
to the farmer or on farm. Farms will be able to be remotely monitored and many
aspects remotely controlled.
Smart irrigation – the use of sensor technology to deliver irrigation where and
when it is required in the appropriate amounts has the potential to vastly reduce
water use in agriculture and horticulture (Fraceto et al., 2016). Smart irrigation
offers the potential to efficiently utilise scarce water resources.
Internet of health (i.e., remote medical technologies and services) – Universal
broadband will enable medical services to be delivered and managed across the
Internet to remote locations (Broadbent, 2016; Hamons, 2014; Milner, 2016).
Virtual medical centres, medical and elder companion robots, with AI capacity, will
provide assistance and care for people in rural and remote locations. AI doctor
bots will monitor an individual’s health, make health and lifestyle
recommendations, and notify authorities in medical emergencies.
E-Fences (new fencing animal containment technologies) – electronic or geo-
fencing enables animals to be kept in geographically defined locations and moved
about remotely by computer - potentially eliminating the need for fencing of
paddocks on farms (Henry, 2016).
Sensors and wireless sensor networks - sensor technologies, as discussed
above, will enable the monitoring of a larger range of environmental and plant and
animal factors providing information for analytics and decision support and
sending data wirelessly to the appropriate platforms for processing by AIs.
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 22
Sensors may be used, located and embedded in a number of different ways
including terrestrial, farm machinery, drones, balloons, planes, satellites, on
animals, on plants, and in fields and soil. Some of their agricultural applications
include providing information regarding plant and animal health and growth, weed
control monitoring, field fertility, soil salination, soil pH, irrigation needs,
temperature, humidity, solar radiation, high resolution land and water mapping,
animal tracking and management, biosecurity surveillance, biodiversity
monitoring, and deployment of agricultural robots. (Barcelo-Ordinas et al., 2013).
Plant and insect recognition technology/software, DNA testing and identification -
machine identification of plants and insects will facilitate decision support and
provide a basis for farm activity automation such as robotic weeding and pest
management (Li, 2014). These technologies have the potential to vastly reduce
pesticide use.
Electronic traceability – will provide a mechanism to verify product provenance,
ethical and environmental credentials (Sugahara, 2009). It also opens up market
opportunities through product storytelling to inform interested consumers.
Decision support software – will be an integration of science models with big data
and unstructured data analysis by artificial intelligence enabling better and more
efficient on-farm decisions delivered to the farmer’s mobile device in the field
(Buckmaster, 2016; Clifford, 2016).
Farm automation – progressively more elements of the farm system will become
automated as sensor technology, science models, AI, decision support software,
and robotics become part of the farm management system (Sukkarieh, 2016).
Drones – drones offer the potential for farmers to remotely observe any part of
their farm from within their home or some other remote location. A range of
different sensors can be fitted to drones and these can provide information to
digital agriculture systems. Drones could be controlled using VR helmets providing
a mixed reality experience, with information and farm advice provided by AI
software superimposed using augmented reality technology.
Robots – robots are increasingly becoming more mobile, flexible in function and
able to work alongside human beings. There is a large amount of research going
into agricultural robotics with some very promising results. Some applications
include milking machines, mechanical weeding, precision pesticide use, precision
fertiliser distribution, horticultural harvesting, and human labour replacement –
including managerial decisions (Sukkarieh, 2016). Social robotics is also a
burgeoning field, with robotic companions, robot home help and medical robots
being potential rural applications already under trial in New Zealand (Broadbent,
2016).
Human ability enhancement tools (e.g., exoskeletons) – exoskeleton technology
enables humans to do hard physical work, lifting heavy loads etc. without physical
strain. This technology could find practical applications on farm enabling a single
man to effortlessly lift large weights and do the work of several men (Lockheed
Martin, 2016).
Autonomous vehicles – already many tractors are GPS controlled and do not
require drivers. Detailed digital mapping of properties along with GPS will enable
fleets of small solar powered robotic vehicles to monitor and work the land. Self-
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 23
driving vehicles (land, air, and sea) will allow driverless transport of products to
processors or markets (Seba, 2014). They may also make rural roads safer for
rural residents, enabling rural social activities to include alcohol without concern
about transport home.
New transportation technologies (pods, hyperloop, drones etc) – Hyperloop
technology, currently under development, may create new, cheap, high speed
avenues for freight transportation around the globe. Timeframes for such
technologies and revision of transportation systems is uncertain (Mazza, 2016).
Unmanned drones are being trialled as delivery vehicles by companies such as
Amazon. In New Zealand, Domino’s Pizza’s has been approved to start the
world’s first drone Pizza delivery service (Bridges, 26 Aug. 2016).
Personal flight technologies (transport drones) – Personal flying devices (which
may also be autonomous) are looking highly probable. These could have a range
of uses for both rural enterprises and rural living – collapsing the remoteness of
rurality (McGoogan, 2016). An autonomous taxi drone, the Ehang 184, has
received permission to be trialled for human transport in Las Vegas.(McGoogan,,
8 June 2016).
New trading venues - Internet market places and cryptocurrencies (e.g., bitcoin)
based on blockchain technology have the potential to radically reorganise value
chain structures, cutting out the middlemen (Tapscot and Tapscott, 2016;
Whitehead, 2016). Producers may use these technologies to communicate and
market products directly to consumers.
Energy farming – renewable energy generation as a farm-based product; solar,
wind, methane (UCS, nd). Climate change is driving the need to produce carbon
neutral energy sustainably and renewably. Energy production either as electricity
to be fed directly into the grid, or some type of biogas generation, could become
feasible revenue streams for landowners as the technologies for production
become cheaper, more efficient and more reliable.
Virtual reality, augmented reality, mixed reality – virtual, augmented and mixed
reality devices are enabled by ultrafast broadband. Much hyped for many years,
VR, AR and MR technologies have finally reached a sufficiently sophisticated and
useable form for popular acceptance. Growth in commercialisation of these
technologies is poised to explode in 2016-7. VR and AR will have a range of
applications including farm management, communication, education, recreation,
entertainment, tourism, medical care, modelling and visualisation. The potential is
to interact with anyone anywhere in a virtual space or even a distal real space
(c|net, nd). Virtual school rooms will co-locate distal pupils in augmented and
mixed reality learning spaces limited only by human imagination. AI teacher bots
will enable individualised teaching instruction based on analysis of a student’s
capacity and current knowledge and skill levels. Virtual offices will similarly enable
co-location of distal workers and collaborators who will be able to interact with one
another, control machines, manipulate objects on the macroscopic, microscopic
and nanoscopic levels, and be tele-present in robots, controlling their function –
eventually just by thought. Entertainment and recreation will similarly be
revolutionised. Already the computer gaming industry dwarfs Hollywood and the
movie industry. People already play on-line games in virtual spaces with other
people from across the globe. The potential for virtual and mixed reality worlds is
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 24
only just beginning. Virtual reality may help to mitigate the isolation that many
people in rural areas feel. The social media giant, Facebook, recently purchased
Occulus Rift, a virtual reality company for SUS2.3 billion (Day, 2105). Augmented
reality devices, such as Microsoft’s HoloLens (Alderman, 2015) may provide a
useful tool for farmers to receive information about their farming operations and
for the delivery of decision support from cloud based AI.
3D printers – 3D printing, or additive manufacturing, is a rapidly developing area
of research and technology – many items that are used on farm on in the home
may be able to be printed on-demand when needed by downloading the
appropriate software from the Internet. A wide range of different things are
currently being manufactured with 3D printing including orthotics, pharmaceutical
pills, houses, artificial bones, skin and organs, components for jet planes, guns,
guitars, electronic circuits and vinyl records to give a sample of the diversity
possible). 3D printing of food is also a developing technology for example the
ChefJet 3D printer use sugar and cocoa butter to create various sweet treats
(Dredge, 2014).
3.2.1 Emerging technologies - new rural products, businesses, and jobs
Although still controversial and subject to some ethical deliberations, technologies such
as GE, gene editing, and synthetic biology offer the potential for the development of new
high value products, some of which may be able to be produced on farm or in
glasshouses. Pharmaceuticals, vaccines and nutraceuticals grown in plants are
examples. Other possibilities include new materials - e.g., large scale production of spider
silk, a super strong material, and biofuels, which can be produced using GE
plants/bacteria/animals or synthetic biology (Scheller et al., 2001).
New technologies will also create new job opportunities in rural industries and food
production. Farming Futures, a joint initiative between IBERS at the University of
Aberystwyth, NIAB, Harper Adams University, East Malling Research, Agri-Food and
Biosciences Institute (AFBI) and SRUC, identified six potential new farming roles they
believe may be common by 2030. The six new roles are geoengineer (carbon capture
biotechnologies), energy farmer (wind and solar electric generation and biofuels), web 3.0
farm host (host to provide customer information about the farm and its products –
provenance and sustainability storytelling – both digital and real), animal psychologist
(holistic animal management using psychology and animal behaviour principles to farm
“freedom food”), pharmer (pharmaceutical farmer), and insect farmer (insects are much
more efficient producers of protein than the animals we currently farm). These new
positions will require a range of new skill sets for future farm workers. New jobs will also
arise in rural communities for the installation, maintenance and repair of digital
technologies, robotics and precision agriculture farm equipment.
The new and emerging technologies discussed above may also enhance the suitability of
rural areas for entrepreneurship in non-agricultural enterprises by providing mechanisms
for reaching and trading on markets anywhere in the world. Thus, for example, people
with careers as diverse as writers, software developers, inventors, artists, musicians,
cheese makers and craftspersons could live and work from rural locations, mix with their
peers in virtual spaces, sell their products in virtual market places, using secure
crytocurrencies (Tapscott and Tapscott, 2016). Technological advances that are digitally
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 25
collapsing space and time, bringing rural communities closer to everywhere, and
removing the isolation of rural life, may attract new residents, refugees from unaffordably
priced urban housing markets who may now find meaningful occupations and lifestyles in
rural areas.
4. Synthetic food
A new revolution is beginning in food production, synthetic food, that is, food made in the
laboratory, food made without animals and without farms. Synthetic food offers the
potential to vastly reduce the environmental impacts of agriculture, in terms of both
pollution and resource extraction. Additionally, synthetic food can be manufactured in very
clean environments helping to eliminate many of the pathogens, antibiotics and hormones
found in real meat and milk.
Cultured milk – it is possible to make milk without the cow. The process uses genetically
engineered yeast to bio-brew milk in vats. The technology is very similar to the process
currently used to make insulin for diabetics which also uses GE yeast (or bacteria) or like
beer which is brewed in a similar fashion. The milk can be “adjusted” so that those with
dairy allergies are able to consume it. Roughly 65% of adults have some difficulty
digesting lactose – while less common in European populations, this problem is
particularly frequent in East Asian populations. It may also be fortified with other vitamins
or nutrients to enhance its health giving potential. Currently cultured milk costs about twice
the price of ordinary milk (Qiu, 2014).
However, once in full scale production, it is likely that the price will rapidly fall and become
significantly cheaper than cow derived milk. There will also be significant environmental
benefits from less intensive dairy farming (Pandya, 2014). A Silicon Valley company,
Muufri, is currently developing the production of cultured milk, which they are initially
targeting at the vegan market. While the fact that a GE organism is involved may deter
some of the public, and some vegans are opposed to GE, this is not a deterrent to other
vegans, such as the three who started Muufri.
Cell cultured meat – it is also possible to grow meat in a cellular process (Datar and
Betti, 2010). In 2013 the first synthetic hamburger patty was produced for a cost of
$US325,000. In 2015 the same item was produced for under $US12 (Crew, 2015). This
technology will continue to improve in quality and reduce in cost. At some point in time
the technology may produce meat that is indistinguishable from meat grown on animals
and is also cheaper. When these conditions occur market segments may open up for the
product. There are also likely to be environmental benefits from producing meat in this
way rather than on-farm. Modern Meadow is a Boston based company that makes lab-
cultured animal products, meat and leather products, without animal slaughter and with
far less water, land, chemical and energy consumption (Rundle, 2015).
Memphis Meats is a company based in San Francisco that is making hotdogs, sausages,
burgers and meatballs from cultured cells of cows, pigs and chickens. The CEO, Uma
Valeti, claims “..cultured meat will completely replace the status quo and make raising
animals to eat them simply unthinkable” (Anon, 2016a, para 3). Mosa Meat is another
company also researching and developing similar cell cultured meat products. These
foods are aimed at both niche and new markets. Niche markets include current consumers
who are uncomfortable about animal welfare conditions in the current agricultural system
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 26
and consumers who are concerned about the negative environmental impacts and
unsustainability of modern agriculture. New markets are vegetarians and vegans who
currently do not consume the products for the above reasons.
Synthetic wine – Ava Winery makes artificial wine copying the chemical composition of
famed real wines by combining water with flavoured compounds and ethanol in a fifteen
minute process. The company claims that the wines will be cheaper and will not have the
same environmental impact or water footprint (Anon, 2016b).
Plant based eggs – Hampton Creek is a company that make products such as
mayonnaise with synthetic plant based eggs which are marketed in major US
supermarkets. Hampton Creek intends to make a range of other products, not related to
eggs, all based on the properties of plants (Kowitt, 2016).
Plant based meats - Impossible Foods makes a plant based synthetic burger patty and
aims to reduce the environmental harm caused by unsustainable agriculture. Recently
they refused a $US300 million buyout offer by Google (Bowles, 2016).
Plant based sea food – New Wave Foods is another Californian company which aims to
manufacture a plant based ‘popcorn’ that looks, tastes, smells and feels like real shrimp
(Khazan, 2016).
Insect protein – not actually a synthetic food, but perhaps similarly foreign to most
westerners, and a food technology likely to grow in importance. Our traditional agricultural
foods, such as meat and milk, in addition to the negative environmental consequences
from modern intensive production, are resource intensive and wasteful to produce in
comparison to producing insect protein. Insects are rich in high quality protein, amino
acids, iron, omega 3 and vitamins. They have better resource to food conversion rates;
crickets are six times as efficient as cattle, four times as efficient as sheep and twice as
efficient as pigs and chickens. They use less water, require less land, produce less GHG
and ammonia emissions and they can be grown on organic waste (FAO, 2016).
More than 1900 insect species have been identified as suitable for human food (FAO,
2016). Entomophagy, the human consumption of insects for food, has been a traditional
source of protein in many cultures for millennia. Although insects are eaten in 80% of the
world’s nations they have not been popular in developed western countries. However,
recently a number of developed world start-up companies are producing and marketing
insect products for human consumption. These include: Chapul, Tiny Farms, All Things
Bugs, Aspire, Exo, Entomo Farms, Micronutris, Jimini’s, and Bugsolutely (Wikipedia,
2016). Anteater is an edible insect food company based in Christchurch
(http://www.anteater.co.nz/).
4.1 The disruptive and transformative nature of synthetic foods
Many of the above synthetic food products may not initially appeal to mainstream
consumers, especially while quality is still lower and while the cost is higher. These foods
are aimed at both niche and new markets, according to Christensen et al. (2015), the
entry point for disruptive technological innovations. Niche markets may include current
consumers who are uncomfortable about animal welfare conditions in the current
agricultural system and consumers who are concerned about the negative environmental
impacts and unsustainability of modern agriculture. New markets may be vegetarians and
vegans who currently do not consume the products due to animal welfare and
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environmental concerns. However, as the quality of the products improves and the cost
drops and genetic technologies become more commonplace, the products may start to
appeal to mainstream customers and become normalised.
Synthetics foods have the potential to revolutionise and transform planetary food
production systems; making it possible to produce milk and meat as required anywhere
there exists a market, improve the quality and safety of food, integrate new healthy
attributes into old products, reduce the cost of food to the consumers, meet the specific
ethical requirements of existing and potentially new markets, while reducing negative
environmental impacts. Nonetheless, to many people working in the milk and meat
industries these products, and the concept that they will be adopted by the public,
currently appears farfetched (Wannan, 12 July 2014). This attitude by incumbent
industries could cause them to be taken unawares. Christensen et al. (2015) claim that
the characteristics that can make an innovation truly disruptive to the incumbent industries
are that it initially aims for new or niche markets (or both) and that the incumbents dismiss
the new innovation as irrelevant to their current business model and mainstream
customers. This appears to be what is happening in the case of synthetic food (admittedly
a rather unappealing name).
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5. Conclusions
One purpose of this paper has been to consider how digital technologies might impact on
the development of rural enterprises and rural lifestyles over the next 10-20 years and
beyond. While prediction is difficult and fraught with unknowns, utilising Gibson’s concept
that “the future is already here, just not evenly distributed” my approach has been to
consider the most recent technological developments and advances at the edges of, or
just beginning to enter, the mainstream of technology and business. My underlying
assumption is that, due to a range of factors discussed above, the future will see many of
these technologies improve in function and performance, reduce in price, and move into
the mainstream.
Before considering emerging technologies, I first identified some important factors
influencing the development of technology in general, and the propensity probability of
mainstream adoption of particular technologies. These factors include technological co-
evolution, technological convergence, technological integration, miniaturization, cost
reduction, the Law of Accelerating Returns, the sigmoidal adoption pathway, disruptive
and sustaining innovations, human collaboration and trust, and responsible technological
development.
Next some key enabling technologies, necessary for the development or application of
many new and emerging technologies, were identified and their relevance to rural
enterprises and communities and the downstream technologies which they enable were
discussed. The enabling technologies identified were universal mobile broadband,
wireless sensors and the internet of things (IoT) or Internet of Everything (IoE), cloud
computing, artificial intelligence, sustainable energy generation and storage,
nanotechnology and material science, biotechnology, and blockchain technology.
Then a range of emerging technologies, mostly rapidly developing digital and bio
technologies, and their potential implications for rural enterprises and communities were
briefly considered. Many of these technologies, although using emerging technologies,
creating change and the need for new skills and knowledge, will largely sustain current
on-farm agricultural production, making agriculture incrementally more efficient. Their
disruptive potential is primarily related to agricultural supply chains in the immediate future
and, in the more distant future, to human labour. Given the current state and progress of
technological development, the major companies and innovative start-ups involved, and
the potential efficiency gains, production and environmental benefits, the wide scale first
world adoption of digital agriculture in the near to medium future seems inevitable.
Many of these emerging technologies will also have significant impacts on rural
communities and the lives of people living there. Digital technologies collapse space and
time enabling instant worldwide communication, bringing rural communities closer to the
rest of the world. A whole range of new activities and services will be opened up to rural
residents: health services, recreation and entertainment, education, and social and
political participation. Perhaps, the truly transformative aspects of digital technologies will
be their impact on human lifestyles, and for rural communities, their digital integration into
the wider social world.
Finally, a truly disruptive and transformative technology, to the current agricultural
production system, as defined by meeting Christensen et al. (2015) characteristics of
disruptive technologies, and Kuhn’s paradigm change, synthetic food, and the companies
developing them were discussed. These companies are producing very fringe products
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 29
most of which have not yet hit the markets, but which have the potential to eventually be
mainstreamed. They are targeted at both new market segments (e.g., vegans and
vegetarians) and niche traditional market segments. In this case, a niche traditional
market segment might be groups who currently enjoy and wish to consume the
traditionally produced agricultural products but feel uncomfortable about the
environmental damage caused by intensive agriculture and/or uncomfortable about the
treatment of animals in the current agricultural system.
In their fledgling form, the companies producing these products are receiving support and
encouragement from the largest, wealthiest and most visionary companies in the high
technology sector of Silicon Valley. They are redefining the ontological and
epistemological boundaries of what food is, how it is produced, and what it means. They
are also redefining the axiological boundaries of food and food production and attacking
traditional agricultural production by exploiting its moral weaknesses. Yet they are largely
ignored as being irrelevant, or dismissed as not attractive to mainstream consumers by
the New Zealand agricultural sector (Wannan, 12 July 2014), which until recently largely
denied culpability for environmental damage (Johnson, 13 Dec. 2013) and still believes
they should be exempt from greenhouse gas restrictions or carbon taxes (Rolleston, 23
June 2014).
Although, as Christensen et al. (2015) note, not all disruptive innovations end up
successfully disrupting and toppling the incumbent industries, the current incumbents
should be thinking about strategies in the 10-30 year timeframe to protect their patch,
clean up their environmental and animal welfare activities, and co-exist with synthetic food
production. The threat or opportunity (depending on one’s perspective) is that, within 30
years, the bulk of milk and meat could be produced without agriculture or animals, and
rural agriculture could potentially be reduced to a fraction of current production. Such a
change could have significant impacts on New Zealand agriculture, the New Zealand
economy, the New Zealand environment, and life in rural New Zealand.
What should New Zealand do to prepare for the rapidly advancing synthetic food
revolution? When cost, safety, quality and food security for the production of synthetic
agricultural commodities surpasses traditional agricultural production, being in the
business, as New Zealand currently is, of producing agricultural commodities will be
extremely challenging. The New Zealand agricultural sector needs to start considering
whether it can survive the disruptive and transformative onslaught about to be unleashed
by synthetic food, and how to remain relevant in the new food production environment.
Aside from jumping on the synthetic food bandwagon, which should be one prong of our
resilience defence (if we wish to continue to be an exporter of commodities), what point
of difference can New Zealand leverage to maintain markets for its agricultural produce?
Clearly, moving away from dependence on the agricultural production of low value
commodities will be essential. It will be necessary to focus on the production of high
quality, value added agricultural products aimed at the higher end, wealthier markets. New
Zealand needs to consider what the market for traditional agricultural products will look
like when synthetic food is cheaper, safer, and equal, perhaps even better, quality. Who
will want to buy agricultural products produced traditionally? What attributes will this
market segment demand from the products?
One prospect is that the wealthy and the very wealthy will take pride and gain status
through the consumption of non-synthetic or “real foods”. Likely, they will demand that
these be of the highest safety and quality and produced by the most ethical processes in
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 30
terms of working standards and conditions of producers and employees, animal welfare,
and environmental care. Clever marketing to wealthy consumers over the next two
decades could reinforce a direction in which their consumer preferences are already
headed and open up a new wealthier market segment for high end quality agricultural
produce. For producers who can meet this particular market segment’s exacting
requirements, there will be a significant price premium, as this market segment is not
concerned about cost. Indeed, higher cost will merely add to the status of these products.
In preparation for this coming future, the New Zealand agricultural sector needs to change
its current primary focus from high volume production to high quality produce.
New Zealand already has a reputational advantage for producing healthy food in a clean,
green, natural environment. Over the next two decades it will be imperative to maintain
this status and reputation. Care will be required to ensure that the typical often short
sighted agricultural industry drive for production and intensification do not negatively
impact our environment and destroy New Zealand’s strategic and reputational advantage.
We need to ensure that the pathway to capture the opportunity resident in these future
high value markets is not closed off for short-term gains. The more natural and organic
the New Zealand agricultural sector can claim to be, and the higher the quality of
agricultural products the better positioned the sector will be to complement the synthetic
food market with high value traditionally produced “real foods”.
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Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 31
6. References
Alderman, N. (2015, 9 February). HoloLens: Get ready to mix the real and the virtual in a
mind-blowing new world, The Guardian, 9/2/16. Retrieved 18/07/16 from
https://www.theguardian.com/technology/2015/feb/09/hololens-microsoft-virtual-
world-augmented-reality-
Anon. (2016a, 1 February). Memphis Meats, cultured meat company profiled in today’s
Wall Street Journal, makes global debut, Press Release 1/2/16 retrieved 18/0716
from http://www.memphismeats.com/press-releases/
Anon. (2016b, 16 May). Synthetic wine made without grapes claims to mimic fine vintages.
New Scientist, 16/05/16, Retrieved 18/07/16 from
https://www.newscientist.com/article/2088322-synthetic-wine-made-without-
grapes-claims-to-mimic-fine-vintages/
Aizcorbe, A., Oliner, S. D., & Sishel, D. E. (2006) Shifting trends in semiconductor prices
and the pace of technological progress, Finance and Economics Discussion Series:
2006-44. The Federal Reserve Board. Retrieved 25/07/16 from
http://www.federalreserve.gov/Pubs/FEDS/2006/200644/
Barcelo-Ordinas, J. M., Chanet, J. P., Hou, K.-M., & Garcia-Vidal, J. (2013). A survey of
wireless sensor technologies applied to precision agriculture, Paper presented at
the 9th ECPA conference, 7-11/07/13, Lleida, Spain.
Bellingham, B. K. (2011). Proximal soil sensing. Vadose Zone J. 10, 1342–1342. doi:
10.2136/vzj2011.0105br
Benson, C. L. & Magee, C. L. (2015). Quantitative determination of technological
improvement from patent data, PloS One 10(4): e0121635. Doi:
10.1371/journal.pone.0121635
Berti, G., & Mulligan, C. (2015). Industry Transformation Horizon Scan: ICT & the Future
of Food and Agriculture, London: Ericsson, Networked Society Lab.
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. London: Oxford
University Press.
Bower, J. L., & Christensen, C. M. (1995) Disruptive Technologies: Catching the Wave,
Harvard Business Review, January-February, pp. 43-53.
Bowles, N. (2016). It looks like burger, tastes like burger – but it’s a plant, The Guardian,
2/06/16. Retrieved 19/07/16 from
https://www.theguardian.com/technology/2016/jun/02/impossible-foods-plant-
burger-taste-test
Bradley, J., Loucks, J., Macauley, J., Noronha, A., & Wade, M. (2015). Digital Vortex: How
Digital Disruption Is Redefining Industries. Global Center for Digital Business
Transformation. Retrieved 30th June 2016 from
http://www.cisco.com/c/dam/en/us/solutions/collateral/industry-solutions/digital-
vortex-report.pdf
Bridges, S. (2016, 25 August). World-first UAV pizza delivery trial for New Zealand.
New Zealand Government press release. Retrieved 10/10/16 from
http://www.scoop.co.nz/stories/PA1608/S00458/world-first-uav-pizza-
delivery-trial-for-new-zealand.htm
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 32
Broadbent, E. (2016). Human robot interaction, paper presented at Bringer Rural New
Zealand Closer: 2016 Rural Connectivity Symposium, Wellington, 28 April.
Brody, P., & Pureswaran, V., (2015). The next digital gold rush: how the internet of things
will create liquid, transparent markets, Strategy & Leadership, Vol. 43 Iss: 1, pp.36
– 41, http://dx.doi.org/10.1108/SL-11-2014-0094
Buckmaster, D. (2016). Digital Ag systems in the US & possible lessons for Australia,
paper presented at Digital Disruption in Agriculture Conference, Sydney, 2-3 June,
2016
Bunge, M. (1977). Towards a technoethics. Monist, 60(1), 96-107.
Carlson, R. H. (2003). The pace and proliferation of biological technologies, Biosecurity
and Bioterrorism: Biodefense Strategy, Practice, and Science, 1 (3): 203-214,
doi:10.1089/153871303769201851
Cherian, S., & Oliveira, M. M. (2005). Transgenic plants in phytoremediation: recent
advances and new possibilities. Environmental Science Technology, 39(24), 9277-
9290.
Chopra, P., & Kamma, A. (2006). Engineering life through synthetic biology. Silico Biology,
6(5), 401-410.
Chowdhury, K., & Bagasra, O. (2007). An edible vaccine for malaria using transgenic
tomatoes of varying sizes, shapes and colors to carry different antigens. Medical
Hypotheses, 68(1), 22-30. doi: 10.1016/j.mehy.2006.04.079
Christensen, C. M., Raynor, M. E., & McDonald, R. (2015). What is disruptive innovation?
Harvard Business Review, December 2015.
Clifford, D. (2016). The role of digital agriculture in future world farming systems, paper
presented at Digital Disruption in Agriculture Conference, Sydney, 2-3 June, 2016.
C|net. (n.d.) virtual Reality 101. Retrieved 15/07/16 from http://www.cnet.com/special-
reports/vr101/
Crew, B. (2015, 2 April). Cost of lab-grown burger patty drops from $325,000 to $11.36,
Science Alert (2/4/15). Retrieved 18/07/16 from http://www.sciencealert.com/lab-
grown-burger-patty-cost-drops-from-325-000-to-12
Datar, I., & Betti, M. (2010). Possibilities for an in vitro meat production system. Innovative
Food Science & Emerging Technologies, 11(1), 13-22. doi:
10.1016/j.ifset.2009.10.007
Day, E. (2015, 11 October). Virtual reality? Not for me. Then I turn into Wonder Woman
and fly over New York. The Guardian, 11/10/15. Retrieved 18/07/16 from
https://www.theguardian.com/technology/2015/oct/11/virtual-reality-oculus-rift-
stanford-silicon-valley-facebook
De Waal, F. (2016). Are We Smart Enough to Know How Smart Animals Are? New York:
WW. Norton & Company.
D’Hondt, T., De Volder, K., Mens, K., & Wuyts, R. (2002). Co-evolution of object-oriented
software design and implementation, The Kluwer International Series in
Engineering and Computer Science, vol 648, Part2, 207-224,
doi:10.1177/001872677602900806.
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 33
Diamandis, P. (2016, 5th June). Lessons from Kodak. The Huffington Post, retrieved 30th
June, 2016 from http://www.huffingtonpost.com/peter-diamandis/lessons-from-
kodak_b_10287774.html
Dowler, E. (2008). “Food and Health Inequalities: The challenge for sustaining just
consumption.” Local Environment 13(8):759-772.
Dredge, S. (2014, 29 January). 30 things being 3D printed right now (and none of them
guns), The Guardian 22/2/14, Retrieved 18/07/16 from
https://www.theguardian.com/technology/2014/jan/29/3d-printing-limbs-cars-
selfies
Drexler, K. E. (1986). The engines of creation: The coming era of nanotechnology. New
York: Anchor Books.
Duggins, A. (2016, 7 March). A bull market? Buying shares in a cow is the latest way to
get your beef, The Guardian, 7/03/16. Retrieved 22/07/16 from
https://www.theguardian.com/lifeandstyle/wordofmouth/2016/mar/07/a-bull-
market-buying-shares-in-a-cow-is-the-latest-way-to-get-your-beef
Editorial. (2016). Anticipating artificial intelligence, Nature, Vol 532, 28th April
Fairtrade International. (2013). Unlocking the Power: Annual Report 2012-13. Retrieved
29/6/16 from
Http://www.fairtrade.net/fileadmin/user_upload/content/2009/resources/2012-
13_AnnualReport_FairtradeIntl_web.pdf
FAO. (2012). Sustainable Diets and Biodiversity, Directions and solutions for policy,
research and action. Proceedings of the International Scientific Symposium
Biodiversity and Sustainable Diets United Against Hunger, 3-5 November 2010,
FAO Headquarters, Rome. Retrieved 30th June 2016 from
http://www.fao.org/docrep/016/i3004e/i3004e.pdf
FAO. (2016). Insects for food and feed. Retrieved 21/07/16 from
http://www.fao.org/edible-insects/en/
Fedoroff, N. V., Battisti, D. S., Beachy, R. N., Cooper, P. J. M., Fischhoff, D. A., Hodges,
C. N., et al. (2010). Radically rethinking agriculture for the 21st century. Science,
327(5967), 833-834. doi: 10.1126/science.1186834
Feynman, R. P. (1960). There's plenty of room at the bottom: An invitation to enter a new
field of physics. Engineering and Science: 22-36 (Feb Issue).
Fraceto, L. F., Grillo, R., de Medeiros, G. A., Scognamiglio, V., Rea, G., & Bartolucci, C.
(2016). Nanotechnology in agriculture: which innovation potential does it have?
Frontiers of Environmental Science, Vol. 4, Article 20, doi:
10.3389/fenvs.2016.00020
Giddings, G., Allison, G., Brooks, D., & Carter, A. (2000). Transgenic plants as factories
for biopharmaceuticals. Nature Biotechnology, 18, 1151-1155.
Gates, B., Myhrvold, N., & Rinearson, P. (1996). The Road Ahead (revised ed.). Penguin
books.
Green. J. (2014). Solar PV Grows Exponentially from 2007 to 2014. Forum for the Future.
Retrieved 13/07/16 from http://www.thefuturescentre.org/signals-of-
change/3089/solar-pv-grows-exponentially-2007-2014.
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 34
Good, I. J. (1965). Speculations concerning the first ultraintelligent machine. Advances in
Computers, 6, 31-88.
Grodal, S., Gotsopoulos, A., Suarez, F. F. (2015). The coevolution of technologies and
categories during industrial emergence, Academy of Management Review, vol. 40,
no 3. 423-445 doi: 10.5465/amr.2013.0359.
GSMA Intelligence. (2014). Understanding 5G: Perspectives on Future Technology
Advancements in Mobile. Retrieved 12/07/16 from
https://www.gsmaintelligence.com/research/?file=141208-5g.pdf&download
Hamons, S. (2014). Rural Healthcare is Failing: A Modest Proposal to fix it. Retrieved
14/07/16 from http://www.forbes.com/sites/netapp/2014/09/26/failing-rural-
healthcare/#10ee617060b7
Hawke, G., R. Bedford, Kukutai, T., McKinnon, M., Olssen, E., & Spoonley, P. (2014). Our
Futures Te Pae Tāwhiti The 2013 census and New Zealand’s changing population,
Royal Society of New Zealand.
Hawking, S.,Tegmark, M., Russell, S., & Wilczek, F., (2014) Transcending Complacency
on Superintelligent Machines. Retrieved 30 June 2016 from
http://www.huffingtonpost.com/stephen-hawking/artificial-
intelligence_b_5174265.html
Hayden, E. C. (2014). Is the $1,000 genome for real? Nature,
doi:10.1038/nature.2014.14530 Retrieved on the 29/6/16 from
http://www.nature.com/news/is-the-1-000-genome-for-real-1.14530
Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007).
Functions of innovation systems: a new approach for analysing technological
change, Technological Forecasting Society 74 (4), 413-432.
Henry, D. (2016). Disruption in agriculture: the real game-changers, paper presented at
Digital Disruption in Agriculture Conference, Sydney, 2-3 June, 2016.
Hsu, T-R. (2002). Miniaturization – A paradigm shift in advanced manufacturing and
education. IEEE/ASME International conference on Advanced Manufacturing
Technologies and Education in the 21st Century, Chia-Yi, Taiwan, Republic of
China, August 10-15, 2002.
ICT International. (nd). Precision agricultural solutions & wireless sensor networks.
Retrieved 12/10/16 from
http://ictinternational.com/content/uploads/2014/05/precision-
AG1.pdf?utm_medium=email&utm_campaign=News%202%20October%202016
%20Aus%20-
%20NZ&utm_content=News%202%20October%202016%20Aus%20-
%20NZ+CID_4cc86ee296dc154cb5e7ebb74d9ea72a&utm_source=Email%20ma
rketing%20software&utm_term=Click%20here%20to%20download%20a%20detai
led%20PDF
IFCITP. (nd). International Food Chain Integrity & Traceability Project Inception Phase
Summary: High Level Solution. Retrieved 29/6/16 from
http://www.apec.org.au/docs/Supply%20Chain%20Risk%20Assurance/121_SCR
A.pdf
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 35
Internet Live Stats (accessed June 2016). Number of websites. Retrieved June 30th from
http://www.internetlivestats.com/total-number-of-websites/#trend
IRENA (2015) Renewable Power Generation Costs in 2014. Report produced by the
International Renewable Energy Agency. Retrieved 29/6/16 from
http://www.irena.org/documentdownloads/publications/irena_re_power_costs_201
4_report.pdf
Isenhour, C. (2012). Can Consumer Demand Deliver Sustainable Food?: Recent
Research in Sustainable Consumption Policy & Practice. Anthropology Faculty
Scholarship. Paper 14. http://digitalcommons.library.umaine.edu/ant_facpub/14
ITU. (2015). Measuring the Information Society Report 2015, International
Telecommunication Union, Geneva.
Jansson, C., Wullschleger, S. D., Kalluri, U. C., & Tuskan, G. A. (2010).
Phytosequestration: Carbon biosequestration by plants and the prospects of
genetic engineering. BioScience, 60(9), 685-696. doi: 10.1525/bio.2010.60.9.6
Jenkins, H. (2006). Convergence Culture, New York University Press, New York.
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A
programmable dual-RNA-guided DNA endonuclease in adaptive bacterial
immunity, Science, Aug 17; 337(6096):816-21.
Johnson, B. (2013, 13 Dec.). It’s time dairying dumped denial. Retrieved 2/6/16 from
http://www.stuff.co.nz/business/farming/dairy/9512943/Its-time-dairying-dumped-
denial
Jonas, H. (1985). The Imperative of Responsibility: In Search of an Ethics for the
Technological Age. Chicago: The University of Chicago.
Joy, B. (2000) Why the future doesn't need us. Wired 8,
Kalkman, J., Mehaba, A., Samrat, B., & Bradley, H. (2015). Emerging Technologies in
Solar PV: Identifying and Cultivating Potential Winners. Arthur D. Little. Retrieved
13/07/16 from http://www.adlittle.com/downloads/tx_adlreports/ADL-Renewable-
Energy-Emerging-PV-Technology.pdf
Karcagi Kovats, A., Katona Koacs, J. (2012). Factors of population decline in rural areas
and answers given in EU member states’ strategies, Studies in Agricultural
Economics 114, 49-56.
Kelly, K. (2014,27 October). The three breakthroughs that have finally unleashed AI on
the world, Wired, 27/10/14, Retrieved 19/07/16 from
http://www.wired.com/2014/10/future-of-artificial-intelligence/
Kelly, K. (2016). The Inevitable. New York: Random House
Khazan, O. (2016, 7 April). A synthetic replacement for shrimp made by slaves, The
Atlantic, 7/3/16, Retrieved 19/07/16 from
http://www.theatlantic.com/technology/archive/2016/04/fake-shrimp/477120/
Kowitt, B. (2016, 3 March). Hampton Creek makes big moves beyond mayo in Walmart
and Target, Fortune, 03/03/16. Retrieved 19/07/16 from
http://fortune.com/2016/03/03/walmart-target-hampton-creek/
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: The University of
Chicago Press.
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 36
Kuhn, T. S. & Hacking, I. (2012) The Structure of Scientific Revolutions (4th Ed.). Chicago:
The University of Chicago Press.
Kurzweil, R. (1999). The Age of Spiritual Machines. New York: Penguin Putnam Inc.
Kurzweil, R. (2004). The law of accelerating returns, In Teuscher, C. (ed.), Alan Turing:
Life and Legacy of a Great Thinker, Springer-Verlag Berlin Heidelberg.
Kurzweil, R. (2005). The singularity is near. New York: Viking.
Lal, R. (2007). World soils and global issues. Soil and Tillage Research, 97(1), 1-4. doi:
10.1016/j.still.2007.04.002
Ledford, H. (2016). CRISPR: gene editing is just the beginning, Nature, Vol. 531, Issue
7593, 10 March.
Lee, S. M., Olsen, D. L., & Trimi, S. (2012). Co-innovation: convergenomics, collaboration,
and co-creation for organizational value, Management Decision, Vol. 50(5), pp.817-
831, DOI:10.110800251741211227528.
Lenk, H. (1983). Notes on extended responsibility and increased technological power. In
P. T. Durbin & F. Rapp (Eds.), Philosophy and Technology (Vol. 80, pp. 195-210).
Dordrecht, Holland: D. Reidel Publishing Company.
Levi, G. (2000). Vaccine cornucopia: Transgenic vaccines in plants: new hope for global
vaccination? EMBO reports 1(5), 378-380. doi: doi:10.1093/embo-reports/kvd103.
Li, J. (2014). 3D machine vision system for robotic weeding and plant phenotyping.
Graduate Theses and Dissertations. Paper 13736. Retrieved 15/07/16 from
http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=4743&context=etd.
Lipsey, R., Carlaw, K., & Bekhar, C. T. (2005). Economic Transformations: General
Purpose Technologies and Long Term Economic Growth. Oxford University Press.
Lockheed Martin. (2016). FORTIS Exoskeleton. Retrieved 15/07/16 from
http://www.lockheedmartin.com/us/products/exoskeleton/FORTIS.html.
Luppicini, R. (2008). The emerging field of technoethics. In R. Luppicini & R. Adell (Eds.),
Handbook of research on technoethics (pp. 1-18). Hersey: Idea Group Publishing.
Mazza, E. (2016, 11 May). High-speed hyperloop hits 116 mph in 1.1 seconds during first
public test. The Huffington Post. Retrieved 15/07/16 from
http://www.huffingtonpost.com/entry/hyperloop-first-public-
test_us_5733d6e2e4b08f96c1823bb8
McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. E. (1955). A proposal for the
Dartmouth summer research project on artificial intelligence. Retrieved 30 June
2016, from http://wwwformal.stanford.edu/jmc/history/dartmouth/dartmouth.html
McGoogan, C. (2016, 8 June). Flying robot taxi to start trials in Las Vegas, The Telegraph.
Retrieved 15/07/16 from http://www.telegraph.co.uk/technology/2016/06/08/flying-
robot-taxi-to-start-trials-in-las-vegas/
Mell, P., & Grance T. (2011). The NIST Definition of Cloud Computing. NIST Special
Publication 800-145. U.S. Gaithersburg: Department of Commerce, September.
Metz, C. (2016, 16 March). In two moves, AlphaGo and Lee Sedol redefine the future,
Wired, 16/03/16, Retrieved 19/07/16 from http://www.wired.com/2016/03/two-
moves-alphago-lee-sedol-redefined-future/
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 37
Milner, M. (2016). Broadband initiatives enabling better delivery of rural healthcare, paper
presented at Bringer Rural New Zealand Closer: 2016 Rural Connectivity
Symposium, Wellington, 28 April.
Moor, J. (2005). Why we need better ethics for emerging technologies. Ethics and
Information Technology, 7(3), 111-119. doi: 10.1007/s10676-006-0008-0.
Moore, G. E. (1965) Cramming more components onto integrated circuits, Electronics
(April 19), 114-117.
Mulhall, D. (2002). Our Molecular Future: How Nanotechnology, Robotics, Genetics and
Artificial Intelligence will Transform our World. Prometheus Books.
Müller, V. C. and Bostrom, N. (2014), Future progress in artificial intelligence: A Survey of
Expert Opinion, in V. C. Müller (ed.), Fundamental Issues of Artificial Intelligence
(Synthese Library; Berlin: Springer).
Murray, F. (2002). Innovation as co-evolution of scientific and technological networks:
exploring tissue engineering, Research Policy, vol 31(8-9), pp. 1389-1403, DOI:
10.1016/S0048-733(02)00070-7).
Nagy, B., Farmer, J. D., Bui, Q. M., & Trancik, J. E. (2013). Statistical basis for predicting
technological progress, PLoS ONE 8(2): e52669. Doi:
10.1371/journal.pone.0052669.
Nakomoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System, retrieved 14/07/16
from https://bitcoin.org/bitcoin.pdf
National Nanotechnology Initiative. (n.d.) Nanotechnology & You: Benefits and
Applications. Retrieved 13/07/16 from http://www.nano.gov/you/nanotechnology-
benefits
Nelson, R. R. (1994). The co-evolution of technology, industrial structure, and supporting
institutions, Vol. 3, no.1, 47-63, doi: 10.1093/icc/3.1.47.
OneWeb. (2016). OneWeb.World retrieved 30/6/16 from http://oneweb.world/
Pandya, R. (2014). Don’t have a cow: Making milk without the moo, New Scientist, 25
June 2014. Retrieved 18/07/16 from
https://www.newscientist.com/article/mg22229750.400-dont-have-a-cow-making-
milk-without-the-moo/
Pardo, J., Flavin, A., & Rose, M. (2016). Cloud Computing, U.S. Department of
Commerce, International Trade Association, Industry and Analysis. Retrieved 30
June 2016 from
http://trade.gov/topmarkets/pdf/Cloud_Computing_Top_Markets_Report.pdf
Pawsey, M. (2016). Making it work, paper presented at Digital Disruption in Agriculture
Conference, Sydney, 2-3 June, 2016.
Pimentel, D., & Sparks, D. L. (2000). Soil as an endangered ecosystem. BioScience,
50(11), 947-947. doi: 10.1641/0006-3568(2000)050[0947:saaee]2.0.co;2
Qui, L. (2014, 23 October). Milk grown in a lab is humane and sustainable. But can it catch
on? National Geographic 23/10/16. Retrieved 18/07/16 from
http://news.nationalgeographic.com/news/2014/10/141022-lab-grown-milk-
biotechnology-gmo-food-climate/
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 38
Reimer, K. (2013, 5th June). What is digital Disruption? Retrieved 3/6/16 from http://the-
big-opportunity.blogspot.co.nz/2013/06/what-is-digital-disruption-part-1.html
Roco, M. C., Bainbridge, W. S., eds. (2004). Converging Technologies for Improving
Human Performance. Springer.
Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E. F., et al.
(2009). A safe operating space for humanity. Nature, 461(24 September), 472-475.
doi: 10.1038/461472a.
Rolleston, W. (2014, 23 June). Why a carbon tax is udderly useless to us. Retrieved
1/6/16 from
http://www.stuff.co.nz/business/farming/opinion/10188332/Why-a-carbon-
tax-is-udderly-useless-to-us
Rosenberg, S. (1992, April 19), Virtual Reality Check Digital Daydreams, Cyberspace
Nightmares, San Francisco Examiner, Section: Style, Page C1, San Francisco,
California.
Rundle, M. (2015, 15 October). Slaughter free leather will make our world ‘more cultured’,
Wired, Retrieved 18/07/16 from http://www.wired.co.uk/article/andras-forgacs-
modern-meadow-leather-wired-2015
Scheller, J., Guhrs, K.-H., Grosse, F., & Conrad, U. (2001). Production of spider silk
proteins in tobacco and potato. Nat Biotech, 19(6), 573-577. doi: 10.1038/89335
ScienceDaily (2016). Battery News. Retrieved 12/07/16 from
https://www.sciencedaily.com/news/matter_energy/batteries/
Seba, T. (2014). Clean Disruption of Energy and Transportation: How Silicon Valley will
Make Oil, Nuclear, Natural Gas, Coal, Electric Utilities and Conventional Cars
Obsolete by 2030. Silicon Valley, California: Clean Planet Ventures.
Singer, P. & Mason, J. (2006). The Ethics of What We Eat: Why Our Food Choices Matter.
New York: Rodale.
Small, B. (2007). Sustainable development and technology: Genetic engineering, social
sustainability and empirical ethics. International Journal of Sustainable
Development, 10(4), 402-435.
Small, B. (2011a). Ethical Relationships between Science and Society: Understanding the
Social Responsibility of Scientists, PhD thesis, University of Waikato.
http://researchcommons.waikato.ac.nz//handle/10289/5397
Small, B. (2011b). Genetic engineering and moral responsibility. In H. A. Barrera-Salda
(Ed.) Genetic Engineering: Basics, New Applications and Responsibilities, Chapter
9, pp.227-256. Rijeka, Croatia: InTech Publishing.
Small, B. H., Chikazhe, T., Botha, N., Tipples, R., and Old, K. (2015). An investigation of
the social sustainability of genetically modified rye grass forage in New Zealand.
Proceedings from the 17th Australian Society of Agronomy Conference: Building
Productive, Diverse and Sustainable Landscapes, 20-24 September 2015, Hobart
Australia.
Small, B., & Fisher, M. W. (2005). Measuring biotechnology employees' ethical attitudes
towards a controversial transgenic cattle project: The Ethical valence matrix.
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 39
Journal of Agricultural and Environmental Ethics, 18, 495-508. doi:
10.1007/s10806-005-0904-z
Small, B., & Jollands, N. (2006). Technology and ecological economics: Promethean
technology, Pandorian potential. Ecological Economics, 56(3), 343-358.
Small, B., Payne, T. & Montes de Oca Munguia, O. (2015). Using collaborative research
to build rural resilience in New Zealand. Paper presented at MODSIM, 21st
International Congress on Modelling and Simulation, 29th Nov. to 4th Dec. 2015,
Gold Coast, Australia.
Spelitis, H. (2105). New app tracks beef from paddock to plate. The Observer, March 17th.
Retrieved 29/6/16 from: http://www.gladstoneobserver.com.au/news/new-era-in-
beef-tracking/2576474/
Spoonley, P. (2016). Rebooting the Regions: Why low or zero growth needn’t mean the
end of prosperity. Auckland: Massey University Press.
Statista. (2016). Number of devices connected to the Internet. Retrieved 30/6/16 from
http://www.statista.com/statistics/471264/iot-number-of-connected-devices-
worldwide/
Steinfeld, H., Wassenaar, T., Castel, V., Rosales, M., & Haan, C. d. (2006). Livestock's
long shadow: Environmental issues and options. Retrieved 25 August, 2009, from
http://www.fao.org/docrep/010/a0701e/a0701e00.HTM
Sticklen, M. B. (2008). Plant genetic engineering for biofuel production: towards affordable
cellulosic ethanol. Nature Reviews Genetics, 9(6), 433-443.
Sugahara, K., 2009, in IFIP International Federation for Information Processing, Volume
295, Computer and Computing Technologies in Agriculture II, Volume 3, eds. D. Li,
Z. Chunjiang, (Boston: Springer), pp. 2293–2301.
Sukkarieh, S. (2016). Digital disruption on-farm, paper presented at Digital Disruption in
Agriculture Conference, Sydney, 2-3 June, 2016.
Sustainable Development Solution Network. (2013). The Structural Transformations
towards Sustainable Development. Background paper for the High-Level Panel of
Eminent Persons on the Post-2015 Development Agenda. Prepared by sustainable
Development Solutions Network: A Global Initiative for the United Nations.
Accessed 28/06/16 from http://unsdsn.org/wp-content/uploads/2014/02/130307-
Structural-Transformations-towards-Sustainable-Development-final.pdf
Tapscott, D., & Tapscott, A. (2016). The impact of the blockchain goes beyond financial
services. Harvard Business Review, May 10, 2016. Retrieved 14/07/16 from
https://hbr.org/2016/05/the-impact-of-the-blockchain-goes-beyond-financial-
services
Tesla. (2016a). Powerpack: Utility and Business Energy Storage. Retrieved 12/07/16 from
https://www.teslamotors.com/powerpack
Tesla. (2016b). Powerwall: Energy Storage for a Sustainable Home. Retrieved 12/07/16
from https://www.teslamotors.com/powerwall
Toffler, A. (1971). Future shock. New York: Bantam Books
Toffler, A. (1980). The third wave: The revolution that will change our lives. London:
Collins.
Report prepared for AgResearch October 2016
Technological Foresight for Rural Enterprises and Rural Lives in New Zealand 40
Turing, A. M. (1950). "Computing machinery and intelligence." Mind LIX(236): 433-460.
UCS. (n.d.). Renewable Energy and Agriculture: A natural Fit, Union of Concerned
Scientists Fact Sheet, Retrieved 15/07/16 from
http://www.ucsusa.org/sites/default/files/legacy/assets/documents/clean_energy/a
gfs_overview_2003.pdf
U.S. Congress, Office of Technology Assessment, Miniaturization Technologies, OTA-
TCT-514, Washington, DC: U.S. Government Printing Office, November 1991
Venter, J.C. (2013). Life at the Speed of Light, London: Little, Brown Book Group
Vinge, V. (1993). The coming technological singularity: How to survive in the post-human
era. Vision-21: Interdisciplinary Science & Engineering in the Era of CyberSpace
Symposium. Retrieved 10 June, 2010, from http://www-
rohan.sdsu.edu/faculty/vinge/misc/singularity.html
Vitousek, P., Ehrlich, P. R., Ehrlich, A. H., & Matson, P. (1986). Human appropriation of
the products of photosynthesis. Bioscience, 36(6), 368-374.
Vorosmarty, C., Lettenmaier, D., Leveque, C., Meybeck, M., Pahl-Wostl, C., Alcano, J., et
al. (2004). Human transformation of the global water system. EOS, 85(48), 509-
513.
Wannan, O. (2014, 12 July). Milk made in Laboratories to hit shelves. Retrieved 1/6/16
from http://www.stuff.co.nz/business/farming/dairy/10258565/Milk-made-in-
laboratories-to-hit-shelves
Wayland, R. (2015). Strategic foresight in a changing world, Foresight, Vol 17 Iss 5 pp.
444-459 Http://dx.doi.org/10.1108/FS-03-2015-0016
Whitehead, M. (2016). Digital technology through the supply chain, paper presented at
Digital Disruption in Agriculture Conference, Sydney, 2-3 June, 2016
Wikipedia. (2016). Entomophagy. Retrieved 21/07/16 from
https://en.wikipedia.org/wiki/Entomophagy
WYSS (n.d.). FAQs: Gene drives. Wyss Institute for Biologically Inspired Engineering and
Harvard University. Retrieved 14/07/16 from
(http://wyss.harvard.edu/staticfiles/newsroom/pressreleases/Gene%20drives%20
FAQ%20FINAL.pdf
Zang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and
research challenges, J Internet Serv Appl 1, 7-18, DOI 10.1007/s13174-010-0007-
6.
