GGSD 2015 - Issue Note 3: The role of new data sources in Greening Growth - the case of Drones by OECD

ISSUE NOTE Session 3

The role of new data sources in Greening Growth – the case of Drones

GREEN GROWTH AND SUSTAINABLE DEVELOPMENT FORUM 14 & 15 December 2015 - OECD, Paris

ISSUE NOTE SESSION 3, 2015 GREEN GROWTH AND SUSTAINABLE DEVELOPMENT FORUM “THE ROLE OF NEW DATA SOURCES IN GREENING GROWTH - THE CASE OF DRONES”

OECD GREEN GROWTH AND SUSTAINABLE DEVELOPMENT FORUM

The Green Growth and Sustainable Development (GGSD) Forum is an OECD initiative aimed at providing a dedicated space for multi-disciplinary dialogue on green growth and sustainable development. It brings together experts from different policy fields and disciplines and provides them with an interactive platform to encourage discussion, facilitate the exchange of knowledge and ease the exploitation of potential synergies. By specifically addressing the horizontal, multi-disciplinary aspects of green growth and sustainable development, the GGSD Forum constitutes a valuable supplement to the work undertaken in individual government ministries. The GGSD Forum also enables knowledge gaps to be identified and facilitates the design of new works streams in order to address them.

Authorship & Acknowledgements

This issue note was prepared for the 2015 GGSD Forum to steer discussion around the theme of Session 3, “The Role of New Data Sources in Greening Growth”. The authors are Tamme van der Wal, Data Scientist at Wageningen UR (Netherlands); Lammert Kooistra, Assistant Professor at GRS Group, Wageningen UR (Netherlands); and Krijn Poppe, Research Manager at Wageningen UR, Agricultural Economics Research Institute (LEI) (Netherlands). This issue note benefited from OECD staff comments including comments from Julien Hardelin, Franck Jésus, Catherine Moreddu and Frank Van Tongeren (OECD Trade and Agriculture Directorate) as well as Kumi Kitamori and Ryan Parmenter (OECD Environment Directorate). It benefited from financial support of German Development Cooperation. The opinions expressed herein do not necessarily reflect the official views of the OECD member countries.

THE ROLE OF NEW DATA SOURCES IN GREENING GROWTH - THE CASE OF DRONES

EXECUTIVE SUMMARY

1. SETTING THE SCENE

1.1 INTRODUCTION 1.2 TECHNOLOGICAL INNOVATION 1.3 EXAMPLES FROM OTHER SECTORS: TECHNOLOGY AND SOME EFFECTS

5 5 6

2. DRONES

2.1 INTRODUCTION 2.2 DRONES BRIDGING THE GAP IN AGRICULTURAL DATA COLLECTION 2.3 THE DISRUPTIVE ASPECTS OF DRONE TECHNOLOGY 2.4 TECHNICAL REQUIREMENTS FOR DRONES SUPPORTING GREEN GROWTH 2.5 LEGAL REQUIREMENTS FOR DRONES SUPPORTING GREEN GROWTH

7 8 8 12 12

3. DATA CAPTURING IN AGRICULTURE

3.1 CAPTURING TECHNOLOGIES 3.2 BIG DATA ANALYTICS 3.3 ADOPTION

14 16 16

4. IMPLICATIONS ON BUSINESSES AND THE FOOD CHAIN

4.1 DATA EXCHANGE IN THE PRODUCTION AND PROCESSING CHAIN 4.2 THE MARKET FOR SOFTWARE, APPS AND DATA IN AGRICULTURE 4.3 DATA INDUCED CHANGES IN THE FOOD CHAIN 4.4 CHANGES IN THE SCOPE OF THE FARM AND FARM ORGANISATION

19 20 22 23

5. IMPLICATIONS FOR GOVERNMENT POLICY

6. RECOMMENDATIONS FOR FUTURE POLICY RESEARCH

7. REFERENCES

EXECUTIVE SUMMARY Emerging information and communication technology (ICT) tools and high-tech systems, like civil drones, create new or improved ways to acquire data about agricultural production processes. Scientific evidence about how these new data sources help farming to improve its resource efficiency and reduce its environmental impact is growing. Its uptake in agriculture is encouraged by the ongoing miniaturisation and lower prices of ICT [including Big Data, internet of things (IoT), sensors, robotics, storage, transmission etc.]. There is excitement regarding the potential of civil drones, but currently only crop dusting / spraying and remote sensing are provided as commercial services. The use of drones for sensing is expected to increase when the translation of sensing data into actionable knowledge is further developed, as this is crucial for creating added value. This is a wider issue as all sensor systems are facing this problem. For farmers to use more data in their management systems, tailor-made strategies must be created that account for the farmer’s individual goals, abilities and constraints. In addition, the use of ICT and Big Data will introduce new entrants and new business models that will ignore existing value chains, as can be seen in other domains with AirBnB and Uber. They will soon make existing businesses obsolete or labelled ‘traditional’. This raises questions as to what extent governments must encourage and / or regulate this development. Large and established players in agriculture, including equipment manufacturers, seed producers and agro-chemical companies already take advantage of Big Data and are creating parallel business models. Issues like data ownership, data use and protection are insufficiently defined, which create unwanted side effects in markets. From the downstream side, farmers get incentives to gather more data and use ICT to improve their products and agricultural processes and create new services based on data. This calls for better exchange of data with dedicated platforms and apps, for which several options arise. ICT will help to produce and market more sustainable food with less waste and pollution, due to lower costs of production, as well as product differentiation and segmentation in markets. This is the “green” in green growth. The “growth” in green growth is in the development and introduction of agro-ICT with specific new markets and changes in the organisation of the food chain. This development reduces current market failures (externalities like pollution) and policy failures (not regulating the internalisation of such social costs) and is another source of increasing welfare (growth). The government can use ICT to better monitor and regulate markets where market failures are not addressed. Changes in markets or the organisation of the food chain and the specific characteristics of new markets for apps and data will require the attention of policy makers. Drones represent a myriad of new ICT related technologies that will change agriculture. Its visibility in media makes evident that the uptake of these new technologies has many faces and requires integrated policies. Government policies can be the key to balance the positive contribution on productivity and sustainability of these technologies against the negative aspects on markets, farms and the food supply chain as a whole.

1. SETTING THE SCENE • • •

SUMMARY Scientific evidence about how High Tech systems help farming to improve its resource efficiency and reduce its environmental impact is growing; ICT technology (including Big Data, IoT, sensors, robotics, storage, transmission etc.) will become smaller and cheaper, thus encouraging its use; and New business models will arise, similar to AirBnB and Uber, that will make existing businesses obsolete or labelled ‘traditional’.

1.1 INTRODUCTION Green Growth is the progress we make towards a green economy, which is defined as “fostering economic growth and development, while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies. To do this, it must catalyse investment and innovation which will underpin sustained growth and give rise to new economic opportunities” (OECD 1). Scientific evidence shows that emerging technologies in ICT, in particular the combination of sensors and robots, are capable of contributing to a more sustainable, green growth in agriculture: significantly reducing the use of water, fertiliser, crop protection agents and diesel fuel. This is operationally known as smart farming, defined as the application of data gathering (edge intelligence), data processing, data analysis and automation technologies on the overall value chain. When jointly orchestrated, smart farming allows for operation and management improvement (analytics) of a farm with respect to standard operations (near real time) and the re-use of these data (animal-plant-soil) in improved chain transparency (food safety) and chain optimization (smart data). Such capabilities will be necessarily supported by Internet of Things (IoT) technologies 2. This paper discusses the potential, the adoption and associated policy issues of new data sources like drones for sustainable green growth. The paper is intended to foster discussion on how to utilise these disruptive technologies to facilitate or even accelerate change and green growth. In particular, the policy aspects of this development are considered.

1.2 TECHNOLOGICAL INNOVATION Computer hardware, including sensor technology, Radio-Frequency Identification (RFID) tags, computation power, storage and transmission capacity, is getting cheaper which leads to new options, like the Internet of Things (the idea that almost everything is connected, from cows to containers and refrigerators). Relevant technological developments for green growth in agriculture include (autonomous) field robots that allow for precise practises in cultivation, promising lower use of water, nutrients, chemicals and fuel, and an incredible variety of sensors providing information on a myriad of aspects. The electrification of tractors is one innovation that can realise such efficiencies, and will soon become standardised across the agricultural sector. Electrified tractors not only allow for the hybridisation of energy, but they provide the opportunity to power electronic sensors and electric motors on every corner of the machine. This will allow for independent wheel steering and speed 1

http://www.oecd.org/greengrowth/48012345.pdf

Source “Smart Farming and Food Safety Internet of Things Applications – Challenges for Large Scale Implementations” AIOTI WG06 – Smart Farming and Food Safety, 2015.

control using traction sensors, thus preventing slip and compaction as well as reducing fuel consumption. Tractor manufacturers work on automation, self-optimising machines, collaborative fleets, and enhanced information driven decision making â&#x20AC;&#x201C; all made possible by the ability to sense and act more directly and to transfer, combine and analyse data in (near) real time. The technology itself has already been around for many years, but the innovation becomes disruptive when new business models arise that apply the technology and make existing practices obsolete (or labelled â&#x20AC;&#x2DC;traditionalâ&#x20AC;&#x2122;). Smart farming, i.e. the application of big data, sensoring and robotisation in agriculture, means that farming will change significantly and both social and economic arrangements need to be reconsidered.

1.3 EXAMPLES FROM OTHER SECTORS: TECHNOLOGY AND SOME EFFECTS Outside agriculture we see similar developments in technology. Smart energy meters in households and industry create a shortcut in the energy supply chain by linking local production to local consumption, based on an incredible network of remote monitoring of individual energy use and thus improving energy efficiency 3. At the same time energy providers get access to detailed information on household energy use. This creates side effects that induce concerns and fears by the general public about privacy, cyber security and what companies might be doing with the information gathered. In other sectors we also see how technology can have an impact on the organisation of the supply chain. An example are changes in the facility market where business models like AirBnB, Uber and Booking.com make use of their mass connectivity to match capacity (sometimes even overcapacity) and individual needs. It provides efficiency through cutting costly intermediaries, and leads to sharing of capacity. It also causes major concerns for regulatory frameworks and the applicability of current rules and costs (e.g. taxes) 4. Both examples can lead to green growth and similar effects are possible in agriculture and food. Measuring and optimising the primary production, timely yield forecasts and leveraging resources and capacities are issues currently under investigation by many scholars and industries. Consumers are tempted by the prospects of personalized nutrition diets based on detailed information on production processes and their own health.

EU Parliament: Briefing on Smart Energy

Edelman, B. G., & Geradin, D. (2015). Efficiencies and Regulatory Shortcuts: How Should We Regulate Companies like Airbnb and Uber?. Harvard Business School NOM Unit Working Paper, (16-026).

2. DRONES â&#x20AC;˘

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SUMMARY Agricultural applications will account for more than 80 percent of the anticipated growth of the commercial drone market, but despite the many opportunities, only crop dusting / spraying and remote sensing are currently provided as commercial services; Regulations are hampering growth in the drone market; The translation of data, acquired by drones, into actionable knowledge is still underdeveloped, but crucial for creating added value.

2.1 INTRODUCTION The term drones is often associated with military use, but the last decade shows a steep increase in civil applications. Although the term drone is mostly used, they are called Unmanned Aerial Vehicles (UAV) in the scientific domain, while from a legislative point of view the term Remotely Piloted Aerial Systems (RPAS) is preferred, in particular as aeronautical regulations expect a pilot. This paper defines drones more broadly to include autonomous systems (without pilot or controller). Industry, scientists and agricultural practitioners all have high expectations from the application of civil drones in agriculture. Also, the drone-industry stakeholders expect a lot from agriculture as a key sector to implement this technology. It is believed that agriculture oriented applications will account for more than 80 percent of the anticipated growth of the commercial drone market in the coming decade (Stehr, 2015; Odido and Madara, 2013). Drones are high-tech systems enabled by miniaturisation and further development of electric sensors, actuators, and robotics, all integrated in a device that has gained enormous attention and attractiveness to new businesses. The increasing attention on drones might leave the false impression that their application has already been mainstreamed. Up to now, there are only two main applications for drones: crop scouting with cameras and spraying. The latter in particular in situations where tractors have difficulties to accessing the crops, like rice fields or steep slopes. The crop scouting, or remote sensing application is creating the biggest interest as it allows farmers to collect spatial data on their fields and crops that can be processed for differentiation of cultivation practices for productivity gains and cost savings. Other applications for drones in agriculture entail logistics (package pick-up / delivery), autonomous automated machines (flying pruning robots or sprayers), and other niche applications like scaring birds and acting as a data relay antenna between field and farm. Besides being a promising technology, the current interest in drones also has a strong utility focus, which is most relevant for this paper, and entails issues around new business opportunities and integration with existing technologies and methods. Also, the adverse effects that might arise from the application of the technology need to be addressed.

Figure 1: Two main applications for drones.

Left: crop scouting with cameras (source Fieldcopter.eu) and Right: Crop spraying (source Yamaha).

This paper will focus on the role of drones in (big) data collection, so as a remote sensor that provides actual information about fields and crops.

2.2 DRONES BRIDGING THE GAP IN AGRICULTURAL DATA COLLECTION Remote sensing information from sensors on satellites, aircrafts or tractors can assist in measuring and mapping of varying biophysical and biochemical traits of crops, classification of different crops, crop growth, and soil mapping, among others. Once collected and processed, the acquired data can be effectively utilized to support decision-making processes in crop management, precision agriculture, yield forecasting and/or environmental protection (Zhang and Kovacs, 2012). Drones share those capabilities and add several advantages including agility and resolution. It should also be mentioned that drones face several challenges in contemporary remote sensing practices, especially regarding airspace, payload and electrical power (range) restrictions. Nonetheless, commercial and scientific interest for drone-based remote sensing and geo-information collection is rapidly maturing. Experts worldwide are exploring drone applications across a wide array of interested fields in an effort to advancing the technology to address various challenges (Colomina & Molina, 2014).

2.3 THE DISRUPTIVE ASPECTS OF DRONE TECHNOLOGY Drones are capable of covering sizable areas in a relatively short period of time at equally short distances from the area and/or objects under study. As a result, drones offer unique and highly desired capabilities that provide auxiliary intelligence for agricultural practices, such as very high spatial (cm) and temporal (frequently and near real-time) resolution imagery, and an unprecedented agility at limited costs and effort.

Figure 2: Example of drone based data collection from a combination of camera systems allowing to characterize both the structural crop traits from digital surface model (e.g., biomass, height) and biochemical crop traits from hyperspectral data (e.g., nitrogen uptake, disease infection)

(source: Suomalainen et al., 2014).

What drones can provide to farmers is a relatively low-cost aerial camera platform, providing near real-time “snapshots” of the farm. Current drone technology uses autopilots for all the flying, from automated take-off to landing. Modern software plans the flight path, aiming for maximum coverage of agricultural fields, and controls the camera to optimize the images for later analysis. The key advantage of drone technology for agricultural applications is related to the improved flexibility in data collection which relates to different aspects: • Independent data acquisition at critical moments within the growing season (e.g., pressure of pests and diseases, nutrient deviancy); • Flexible agile deployment is an important asset of drone data collection especially compared to satellites and manned aircrafts, also to overcome clouded sky situations that would prevent satellite imagery; • Customised sensors depending on the anticipated information need. This has already resulted in the development of several new commercial drone-specific sensor systems (Colomina & Molina, 2014); • Increased spatial detail observed with drones will move remote sensing up to the scale of individual plants or to the leaf level (1-10 cm), providing a spatial overview at a detail comparable with handheld or in situ sensors.

The main categories of uses, mentioned in the literature are: • Remote Sensing: to support growers with information to quantify and evaluate effects of their agricultural management (Figure 2). And with emerging software platforms, drone based data is combined with satellite data, ground sensors and in situ sensors into a wholefarm information system. This aims at helping farmers to optimise their daily activities and create higher margins; • Spraying: A favoured application of drones in Japan and Korea to distribute crop protection agents in tractor-unfriendly environments like (wet) rice fields and steep slopes. • Working Horses: An example is the AgroDrone (Figure 3) with a carrying capacity up to 80 kg which could be used for spraying applications (e.g., fertiliser or crop care agents) but with a robotic arm it could also remove diseased plants to avoid spreading of diseases, or prune fruit trees at places difficult to reach by the grower from the ground; • Pick-up / delivery: The drone as a pick-up/delivery robot is mentioned for packages in cities, but will also work out well in the rural area, for instance for delivery of medicines to livestock farms; • Inspection: Another application of drones is to assist in inspection of agricultural infrastructure, in particular for inspection of locations that are difficult to reach, like rooftops, glass houses and installations. Using dedicated camera systems, potential issues can be detected to prevent machine shut down or dangerous worker situations. Figure 3: Work horse drone with 80 kg carrying capacity, here imagined as precision sprayer

(source: drones4agro [http://www.one-man-drone.com/]).

Figure 4: Example of use of drones for inspection of solar panels

This application could be transformed to glass house inspection detecting cracks with Thermal Infrared camera.

• Cattle inventory: Applications similar to inspections are mentioned for herding cattle, to have a real-time camera feed from a drone to keep an eye on the animals; • Crop loss monitoring: as a tool to support private and public crop insurance schemes; Current practice is often based on expert assessment, drone based data collection supports this practice and would allow for the quantification of parameters like surface area and degree of damage; • Government inspections and land administration: Drones are not only relevant for farmers. Governments who are doing a lot of data collection on farms (i.e. environmental inspections, land registration etc.) can benefit from drones to create a fast ‘situational awareness’ during their visits and can improve actuality and completeness of land administration systems (see text box). Box 1. Drones revolutionise land administration Tenure security has a marked effect on expectations of a return on an investment of both labour and capital. Many development thinkers have attributed the weakened incentives to invest in smallholder (small scale, subsistence) agriculture to the absence of security of tenure to land ownership. The core of land administration and tenure security is cadastral information and agreements on location of property boundaries. Land surveying has a long history in registration of data on property boundaries. Drone collection of very detailed aerial photographs and digital surface models (DSM) could provide a rapid and cost effective means of extracting topographic and cadastral information for mapping the detailed land-use patterns in smallholder agricultural systems.

Drones are the most recent development in capturing farm data. Compared to satellites, drones are much more agile and can capture much higher detail (plant / leaf level). A drone flight can be dedicated to one farm, or even one field. This makes it flexible and dedicated. However, the costs for operating drones and processing data are still much higher than that of satellite imagery. But as clouds are an unreliable factor in that acquisition process, drones are a welcome alternative to collect images at critical moments in the growing season. Compared to manned aircraft aerial photography, the advantages of drones are mostly a matter of scale. The use of a manned airplane comes at a considerable cost that will make imagery for smaller areas too expensive. At larger scales (proposed is >1000 ha of adjacent farm land), manned flights may become economically viable. Around the world there are several companies that deliver agricultural imagery, in particular large agricultural areas or high cash crops (e.g. vineyards, fruit trees) or countries with less restrictive aviation regulations.

2.4 TECHNICAL REQUIREMENTS FOR DRONES SUPPORTING GREEN GROWTH Although the drone and camera technology is continuously innovating (in particular endurance and autonomy are currently focus areas of development efforts), the extent to which drones will be disruptive will depend on the capabilities to transform collected data into meaningful information for users such as farmers, contractors, buyers, suppliers (seeds, agro-chemicals, machines), and governments. Two components need further development to support an effective use of dronecollected data: 1)

Method development and large-scale datasets for training and testing of machinelearning algorithms to transform measured data into relevant, timely and spatial-explicit crop management information; Organisation of large-scale data infrastructures for big data coming from drones to connect users, drone service providers and agronomic specialists for timely delivery of crop management information;

Furthermore, the following requirements support a faster and deeper penetration of drones and their capabilities in agriculture: 3) 4) 5)

Dissemination of remote sensing best practices for deriving high-quality sensor data; Developing agronomic knowledge to transform drone-based sensor data to relevant information usable for growers (also in the form of task maps/alerting services etc.); Better cooperation of a broad range of disciplines (inter or trans-disciplinary) is required among drone and sensor/optics engineers, remote sensing, agronomists, ICT system architects etc.; and Create spillovers and learn from other application fields which employ drones (e.g., military, logistics) and transform that to support agriculture.

2.5 LEGAL REQUIREMENTS FOR DRONES SUPPORTING GREEN GROWTH The legislation to operate drones is currently a major barrier for rapid growth. While technology enables autonomous systems, current regulations require a pilot that has his eyes directly (or indirectly via an observer) on the drone. The requirement for a pilot for flying drones (in this domain not unintendedly referred to as Remotely Piloted Aircraft Systems) originates from the airspace regulations intended for transport safety. The role of the pilot is crucial in those regulations. Globally, flying drones (except for recreational purposes) requires pilot licenses, airworthiness certificates and operations manuals. In case of the introduction of drones in airspace, initially national aviation authorities have formulated national specific regulations which often included a ban on the professional use of drones (e.g., the Federal Aviation Administration in USA). Concomitantly, the Convention on International Civil Aviation of the International Civil Aviation Organisation (ICAO) is aiming at harmonising the airspace regulations at the international level. However, this process will take several years to complete. In Europe, the European Aviation Safety Authority (EASA) is the competent authority for drones with a weight of more than 150 kg (maximum take-off weight). Smaller drones are regulated by national authorities. This creates a very fragmented regulatory framework. The European Commission (EC) has now tasked EASA to develop a set of European rules for drones. This will create common safety regulations for the European Union (EU). The EC has asked for a regulatory approach that becomes proportionate to the risks they aim to address. This could imply that drone use for agricultural applications could be subject to adjusted (read: relaxed)

safety regulations compared to urban applications. As the applications for drones in agriculture increase, pushing for these relaxed regulations may support the adoption of drones and hence stimulate greener farming practices. International fragmentation of regulations and rules applicable to drones is enhanced with different approaches across countries, for example in the USA and Australia.

3. DATA CAPTURING IN AGRICULTURE • • •

SUMMARY Several actors in the food chain are already making use of advanced data capturing techniques and ICT and experimenting with new developments; Depending on the farmer’s individual goals, abilities and constraints, tailor-made strategies need to be defined on data use in their management practices; and As long as data ownership is not well defined, some large players will have exclusive access to Big Data, and monopolize knowledge development.

3.1 CAPTURING TECHNOLOGIES Over the last decade the use of ICT technology in the farm sector has increased significantly (Henten et al, 2009). Poppe et al (2013) highlight a range of areas in the agriculture and food sector where ICT has been successfully applied. These include: • Use of satellite, drone, and connected tractor data to precisely control field operations, making it possible to increase labour productivity by increasing the size of machines; • Processes for combining remote sensing data on crop growth and farm data on crop interventions (and ex-post yields) leading to more informed decision making; • Wiring of glasshouses with sensors and computers to steer the production process in an optimal way; • Introduction of robotic milking on family farms in North Western Europe where labour is expensive and farmers are highly educated; • Increased use of sensor technology: cows increasingly are measured as intensely with sensors as sport athletes; sensor data being much better than the human eye at predicting diseases or the optimal time for insemination; • Tracing and tracking (using bar codes and RFID chips) has become standard in agrilogistics; • Retailers are increasingly using apps on smart phones to support consumers and to increase brand loyalty; and • Establishment of on-line shops by farmers due to sharp falls in prices of delivery services as a result of liberalisation of post and parcel markets. This list of examples shows that several actors in the food chain are already making advanced use of ICT and are experimenting with new developments. However, this is just the start of what could become a revolution in agriculture and the food industry, not unlike the wider adoption of the tractor and the introduction of pesticides in the 1950s. It will change the way farms are operated and managed and it will change both farm structures and the wider food chain in unknown ways – just as in the 1950s the extent of the changes in the next three decades are unpredictable. Such changes have been very beneficial for society and the farmers. Innovations have led to more efficiency (often measured in total factor productivity) which means lower prices for consumers and hence higher welfare. It also contributed to more production, improving food security. For farmers the higher efficiency also included higher labour productivity which helped to raise their incomes in line with other sectors in the economy. This helped to make farming an attractive living for the next generation, although the number of farms declined in this process. Where in the second half of the last

century the Green Revolution (better genetics) and mechanization played big roles in this process, we might now see ICT as a driving force (in addition to genetics). ICT also enables farmers to improve their environmental impact. In many cases ecology and economy go hand in hand, like in reducing inputs (water, fertiliser, chemicals, seeds etc.), emissions while maintaining or improving soil conditions. In many countries limits to environmental impacts are set by regulations, which create high incentives for farmers to maintain their so-called ‘license to operate’, as the consent from government, neighbours and buyers that their operation is complying with societal demand. Based on the notion that ICT will be a driving force, the European research funds have put together a coordinated fund to stimulate research and knowledge transfer on ICT use and hence contribute to improve farming practices. The 5 ERAnet ICT-Agri (a European Network of Research Funders in Agro-ICT) has come up with the following strategic research agenda for agriculture. The Farm Management and Information System (FMIS) is to serve as the back-bone for all other ICT and robotic solutions. FMIS provides a common user interface across solution domains and a repository for farm information. It includes tools for communication and information exchange, and acts as a decision support system (DSS). Time-consuming and error-prone manual data collection may be replaced by automated information collection and storage. The FMIS of tomorrow will be a modular system. Variable-rate application (VRA) is the site-specific application of fertilizers, pesticides or water. This improves resource efficiency and environmental performance. The incorporation of FMISs and DSSs in web-based approaches is a particularly important objective. Controlled-traffic farming (CTF) enables the geo-positional control of tractor movements in order to optimize yields and input and reduce negative environmental impacts. Examples are the so-called tramlining (keeping machines in specific tracks to restrict compaction of the soil to those tracks) and coordinated movements of harvesters and trucks to transport the produce (time and fuel savings). Further experiments under different soil and climatic conditions are required. Precision livestock farming is based on sensor measurements as well as on advanced ICTs. The sensitivity and specificity of biosensors must be improved. Advanced systems for automated in-door climate control reduce energy consumption and greenhouse-gas emissions, as well as improve the environment in greenhouses and buildings for livestock. Quality, safety and traceability of food and feed are the main objectives of automated quality control. Sample-based quality control is currently common practice, but future technologies should enable close monitoring of individual product quality. Agricultural robots can replace humans in the performance of manual labour – notably in the case of hazardous or tedious work – in order to improve safety at work, labour ergonomics and efficiency, product quality, and environmental sustainability. Advances in robotic engineering must be applied in the agricultural sphere in order to step up innovation. 5

Text taken from EU SCAR AKIS (2015) forthcoming that based the text on Executive Summary Strategic Research Agenda ICT-AGRI; Didelot et al. (2012)

In addition to these points, the EU’s Future Internet Public Private Partnership has stressed the fact that the use of ICT will accelerate due to cloud technology that makes digital exchange of data easier. Open data (in which governments or others share their data free of charge) can be seen as an example. Together with the Internet of Things (using data from sensors, machines and other devices) and the use of data from social media this contributes to the era of big data.

3.2 BIG DATA ANALYTICS With the Internet of Things, society has entered the era of Big Data Analytics, which “refers to the strategy of analysing large volumes of data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors and sales records. The aim in analysing all the data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights. Through this insight, businesses may be able to gain an edge over their rivals and make superior business decisions.” 6 Big Data analytics will deliver a next step in farming mainly by connecting information across farms (Lowenberg-DeBoer in Long et al, 2015). Therefore, a major role is seen for agribusiness companies that can generate across-farm information and create meaningful information out of these large quantities of data. Monsanto’s Fieldscripts 7 programme (based on the acquisition of Climate Corporation) is an example on how Big Data can and will play a role in increasing yields and making agriculture more efficient. A major concern for Big Data is the lack of incentive for farmers to collect high quality data that can also be used off-farm (Lowenberg-DeBoer, 2015). Although sensors can help farmers to collect and transmit data, knowledge about their context (metadata) is required to understand and curate the sensors’ output. An incentive can come when farmers get benefits from the off-farm use of their data, like better prices or analysis that could help to improve their practices. Overall, farmers need effective strategies on data use in their management practices. Depending on the farmer’s individual goals, abilities and constraints, they can develop a strategy in an optimal way. Data ownership, and the perceived uncertainty on data use, is another major barrier in the adoption of Big Data Analytics concepts and technologies. Farmers’ concerns centre on the issues of privacy and (lack of) control on data use within or beyond the first line. The current paradigm is that all data generated on the farm is the ownership of the farmer, who dictates what and how it will be used elsewhere. As long as data ownership is not well defined, some large players will have access to Big Data, and monopolise knowledge development.

3.3 ADOPTION The use of drones or new data acquisition techniques in a wider perspective is a new domain for which adoption studies are scarce. To start understanding how these disruptive technologies might be adopted, this paper takes a wider look at ICT and Precision Agriculture adoption, as they are closely linked to big data and drones. Precision Agriculture is popularly defined as “doing the right thing, at the right time and the right place” and refers to location specific (or animal specific) treatments, enabled by ICT. Long et al. (2015) provided an overview in the frame of Climate Smart Agriculture. Their literature and empirical research shows that despite the availability of technologies and 6

Techopedia.com

www.fieldscripts.com

practices, factors other than technological barriers are hampering wider deployment and adoption. A literature review examined previous research on barriers to the adoption and diffusion of technological innovations within the agricultural sector as well as others, with a focus upon examples within developed economies (for instance barriers to the adoption of energy efficiency measures in housing or the adoption of hybrid cars etc.). Based on these studies, the existence of a range of socioeconomic barriers to the adoption of technological innovations in agricultural was confirmed. The main adoption barriers on the supply side (technology producers) are: • • • • •

difficulty in proving value and demonstrating impact; lack of knowledge of, and access to capital/investment; unfriendly regulatory landscape; product is too expensive / ROI (return on investment) periods too long for customers; and access to and reaching customers.

Adoption barriers at the demand or technology user side: • • • • • • • •

low awareness of available technologies and inaccessible language; high costs & long ROI periods; lack of verified impact of technologies; regulatory and policy issues; hard to reach and train farmers; R&D and policies do not match the ‘on-the-ground’ reality; low consumer demand; and unequal distribution of costs / benefits across supply chains.

In a recent paper (2015) Lowenberg-DeBoer 8 describes extensive research into successful emerging technologies in agriculture. In this paper, wide adoption is shown for technologies that are characterised by “embodied knowledge”, meaning that the scientific advancements were contained within and therefore the technologies are easy-to-use. The in-depth analysis on precision agriculture for the European Parliament, the European Commission concludes: “The assessment and quantification of environmental benefits is almost totally lacking in the literature. Some farmers do consider these benefits as part of their overall viability decision, based upon their personal values. But apart from general qualitative statements there is no quantified environmental benefit assessment that can underpin an investment decision: this appears a significant omission that could be addressed by developing a methodology and/or tool to be available for the decision process” (Zarco-Tejeda, 2014). Along the same line, the focus group on Precision Farming of the European Innovation Partnership ‘Agricultural Productivity and Sustainability’ advises to develop Precision Agriculture Calculators to allow farmers (or their advisors) to make a cost/benefit analysis. Although fragmented and mostly qualitative, scientific evidence on the impact of Precision Farming and Precision Livestock Farming technologies does exist. Heikkila et al (2012) for instance, studied the uptake of milking robots in Finland. They concluded that the introduction of robots on a farm creates an average increase in productivity by 7%. Claims by manufacturers also highlight the animal welfare aspects with the use of milking robots. 8

Long, T. B., Blok, V., & Coninx, I. (2015). Barriers to the adoption and diffusion of technological innovations for climatesmart agriculture in Europe: evidence from the Netherlands, France, Switzerland and Italy. Journal of Cleaner Production.

The Precision Farming sector furthermore has several claims in arable agriculture. The introduction of GPS for machine guidance is expected to bring about an efficiency gain ranging from 4-20% 9 due to the avoidance of gaps and overlaps. Another 15% is accounted to Variable Rate Application. If one calculates this for instance for potatoes, these gains have an effect on the costs for plant material, agrichemicals, water, diesel and labour. The Joint Research Centre of the European Commission recently commissioned a project to measure the (potential) effects of precision agriculture technologies on GHG emission reduction. This will create evidence on where and how farming can contribute to mitigation and how policies can have a positive effect on supporting precision farming. A more intensive use of information, like weather forecasts, irrigation and fertiliser advice, and market prices etc. also boosts productivity in developing countries. This is reported in many studies at local or national levels. Farmers have sometimes skipped several steps in mechanisation and automation and now with the rise of mobile technology become front runners in paid SMS and voice response services. Where 3G/4G telecommunications are available, the rise of smart phone apps is increasing. Farmers also use their mobile phones for knowledge sharing. The Dutch government started the Geodata for Agriculture and Water programme (G4AW) 10 for applications that improve food security in developing countries with the use of satellite data. This programme is capitalising on telecom networks to get satellite-derived information and advice to farmers. Box 4. The Netherlandâ&#x20AC;&#x2122;s top sectors approach for innovation in the business sector Motivated by concerns over international competitiveness and emerging social challenges, the Dutch government announced the top sectors approach in February 2011. This new form of industrial policy focuses public resources on specific sectors and promotes co-ordination of activities in these areas by businesses, government and knowledge institutes. The nine area chosen (which do not correspond exactly to industrial sectors in established classifications) represented strong economic sectors: agri&food, horticulture and propagation materials, high-tech systems and materials, energy, logistics, creative industry, life sciences, chemicals, and water. In 2011 these sectors accounted for over 80% of business R&D and for just under 30% of value added and of employment. Whereas traditional approaches to industrial policy are government-centred, industry representatives are at the centre of the co-ordination process in the top sectors. For its part government undertakes to develop sector-specific policies across ministerial portfolios, including education, innovation and foreign policy, as well as regulatory burdens. The relevant policy also envisages reducing the administrative burden for businesses, uniting the henceforth disparate channels of public support to businesses with a one-stop shop for service delivery (the so-called Ondernemersplein).

Being strategic and visionary does not stop with a well-crafted strategic vision or plan. It continues with managing policy instruments, and portfolios of policy instruments with respect to the objectives and system boundaries that have been set, while at the same time acknowledging the need for flexibility in the choice and design of policy instruments.

Personal Communication with dr. Spyros Fountas

g4aw.spaceoffice.nl

4. IMPLICATIONS ON BUSINESSES AND THE FOOD CHAIN • • • •

SUMMARY Farmers get incentives from the food chain to gather more data and use ICT; The exchange of data has to be organised with platforms and apps, for which several options exist; The use of ICT might change the governance of the food chain and business models in farming; and ICT contributes to supply more sustainable products.

4.1 DATA EXCHANGE IN THE PRODUCTION AND PROCESSING CHAIN In agriculture, being a sector with many small players, farm data is rarely shared in a seamless way with suppliers, advisors or the processing industry. These business partners have a growing need to connect to the digital data of farmers: food processors ask farmers to collect data to improve their planning and logistics, support tracing and tracking and to substantiate sustainability claims at the retail level. Input suppliers like machinery companies add ICT-based services (like prescriptive farming and predictive maintenance) to their hardware (Figure 5). Better data sharing between business partners (including players who transform data into relevant information – including the government) is needed to address challenges in the food chain. But sharing data between organisations is problematic, as uniformly accepted standards are lacking and interoperability is very low. Imagine for instance the challenge for a large dairy cooperative, that wishes to exchange digital data with 10,000 farmers, or for a manufacturer of milking robots that wants to monitor operational data from products that are sold to farmers and use the farm data to give farmers advice. The issue is even more complex, if one realizes that the data exchange between, for example farmers and their cooperative or robot supplier, leads to digital data that has to be used by third parties: the accountant requires access to the electronic invoices of the cooperative, while the veterinarian and the herd book need access to the data from the cows milked by the robot. A key issue is whether these systems will be proprietorial developed (for example, by the global players in the food chain) or whether they will be more ‘open’ systems.

Figure 5: Business and societal challenges and their ICT solution in the food chain

GRIN: Genetic, Robotic, Information and Nano technologies. Source: Poppe et al 2013, adapted.

Actions to facilitate, standardise and mainstream data exchange will be a major challenge that needs to be addressed in order to benefit from the Big Data Analytics as well as other data driven business opportunities. The potential is shown for instance by the example of Facebook â&#x20AC;&#x201C; a similar sharing platform is required as an infrastructure for this type of data exchange.

4.2 THE MARKET FOR SOFTWARE, APPS AND DATA IN AGRICULTURE For many years there has already been a market for software dedicated to agriculture. Almost without exception, the software suppliers operate in market and domain niches: dedicated pieces of software linking a farmer to a specific supplier or processor. Farmers with multiple links to the markets get overwhelmed with software, without standardisation and a high redundancy in data entry, storage and analyses. The emergence of cloud based systems push these software companies (and their customers) into improved interoperability. As mentioned, Facebook and other consumer based software have paved the path for better integration and demonstrated how this can work. In agriculture, there is a shift towards Agri-Business Collaboration and Data Exchange Facility (ABCDEFs) like the platforms of input suppliers [machine manufacturers and precision agriculture equipment providers like John Deere (MyJohnDeere), Claas (365FarmNet) and Trimble (ConnectedFarm)], inputs providing cooperatives [like Agrifirm (Akkerweb), Invivo], breeding organisations (like CRV) and food processors establish an infrastructure to exchange or to give better access to central data for software-providers. In these cloud-based platforms the users (like SMEs and

farms) can make the data they control available to apps. These apps can be bought (or downloaded free) in an app store, like those currently available on mobile phones. Apps will replace some of the functionality of farm management information systems (as well as adding new functionality). Such apps can be built more cheaply thanks to standardised generic software-components. This implies that app builders do not have to worry about organising access to the data, as long as they use the data standards by which farmers access their data. In this development of platforms (ABCDEFs) there are still major uncertainties on how this will play out in terms of new markets and interoperability. Many farmers will work with more than one data exchange platform. Platform-operators therefore have to make decisions on the scope of their platform: deciding on which other company platform should they make data exchange possible? Farmers will have an interest to pay only once for basic service like precise weather forecasting. App builders might have an interest in selling their app on more than one platform. A project (FIspace) in the EUâ&#x20AC;&#x2122;s Future Internet Public Private Partnership has proposed to solve these issues with an open source, non-commercial integration service based on event processing and standards from organisations like AgGateway (USA), AgroConnect and Frug-i-com (both Dutch), GS1, UNCEFACT and others (figure 6). Figure 6: Data Exchange Platforms from input suppliers and food processors with options to link farmers and independent app developers using a non-commercial integration service.

4.3 DATA INDUCED CHANGES IN THE FOOD CHAIN The availability and exchange of (big) data will have a significant impact on the food chain. First, farmers will see new services like prescriptive farming (using variable rate application) and predictive machine maintenance. Other important changes include: i) the end-to-end tracking and tracing and virtualisation of food chains, and ii) the emergence of direct farmer-consumer markets supported by ICT. The most obvious change is that products get accompanied by information about their origin and production including a full history of used resources and their treatments. It will make it easier for retailers to demand greener (more sustainable) products and monitor such claims of producers. This implies that apps for consumers can provide information on the product, all the way back to the grower of the product and its seeds. This even holds for complex products like pizzas that are made of many ingredients. This will lead to more influence from downstream business partners on farm decision-making. The influence could be through the provision of advice or by tighter contract stipulations. In addition, service level agreements by advisors or, for example, companies that sell machines are possible. The data that is exchanged can also be used for real-time virtualisation: through sensing of physical objects at different levels of aggregation (e.g. product, box, pallet, container, truck), rich and globally accessible virtual representations of these geographically dispersed physical objects can be created (Verdouw et al., 2013). Virtual representations can be visualised for instance to ‘walk through’ the supply chain and visualise what is going on at any stage at any moment, and also place it in the context of its historical development (Poppe et al, 2013). When combined with models, business rules and/or algorithms, virtualisation helps to monitor and predict product quality, for instance like the ripening of fruit or other quality inspection of food and flowers. This information can be brought to inspectors or store managers (or even customers) by Google Glass or any other augmented reality system. Data sharing will also make it possible to add more (computer) intelligence to the chain, including monitoring, problem notification, deviation management, planning and optimisation. This development can be characterised as green growth as it will contribute to greater levels of sustainability and productivity: food processors, retailers and consumers can trace products to their source and investigate the different aspects of sustainability of individual products or batches of products. They can give feedback to farmers, by rewarding higher levels of sustainability. Food waste can also be reduced. Thus, big data sharing can lead to new business models and new opportunities for farmers to differentiate their production. Despite early adopters like Peter’s Farm (www.petersfarm.com ), it is unclear when digital Track and Trace as well as the real-time virtualisation services will become standard. It will introduce new ICT suppliers (sensors, software), auditing firms and services companies. As transaction costs change with such ICT solutions, it will impact the way the food chain is organised. In some cases, this will even lead to totally new chains that replace current ones. For instance, online auctions could sell the fish when the fisherman’s boat is still at sea. By reducing transaction costs, ICT also enables new collaborative arrangements such as local-for-local food webs that deliver local, often organic, food products to local consumers, restaurants or health care institutions.

4.4 CHANGES IN THE SCOPE OF THE FARM AND FARM ORGANISATION As with previous technological developments, not all farmers will invest in new skills (LĂ¤pple et al, 2015, Islam et al, 2013). An interesting question is to what extent these developments in ICT will exaggerate differences between farms, for example will they be scale-neutral or benefit larger farms more than smaller ones â&#x20AC;&#x201C; which has been the case with innovations in the past, especially ones that improve labour productivity. In existing food chains farmers have to invest in data gathering and farm management information systems to satisfy demands from food businesses and retailers for tracking and tracing and quality assurance schemes like GlobalGap. These developments favour larger farms. A second unfavourable aspect for small family farms is the fact that the monitoring of agricultural processes will greatly improve. One explanation for the strong position of family farms is that agricultural production processes are difficult to observe (Allen and Lueck, 2002), which leads to moral hazard and an agency problem: the investor cannot monitor the farm manager and is faced with the question of if the manager is correctly blaming the weather or diseases for the disappointing results (or whether they are shirking their responsibilities). In the same way the manager wonders if the farm worker is working conscientiously in the field far away from the farm office. This way of thinking implies that the transaction costs of monitoring to address the agency problem determine the organizational form: it is a trade-off between specialisation via the market or addressing moral hazard problems through doing it yourself. This also implies that some future trends may favour large nonfamily farms. Thirdly, activities could disappear from the farm when they become automated. Computers of breeding cooperatives already determine by sensors if a cow needs to be inseminated, bypassing the farmer who traditionally had that role. Taking this idea further could imply that some value added activities, like advice, move from the most remote rural areas to regions with clusters of knowledge where they are provided by using ICT. For example, it is more likely that the apps for the farmers are built in Berlin or Wageningen than in a remote area in Bulgaria. This raises major questions as to whether the already significant imbalance of power in the food chain (see for example Renwick et al, 2012) will be further exacerbated. The analysis does suggest that the era of big data will be disruptive regarding the structure of farming. These trends towards larger farms and more professional management, based on ICT and big data, support green growth: total factor productivity improves, leading to lower consumer prices (and hence more welfare) and more sustainability. In that sense the development is beneficial. However, the analysis also show that it leads to a different organisation of the food chain and of the family farm. It will redefine the job of a farmer, just as steam technology (leading to dairies moving cheese and butter production from the farm) and combine harvesters (introducing specialised contractors) did. This implies costs of change and potentially also resistance to change.

5. IMPLICATIONS FOR GOVERNMENT POLICY •

• •

SUMMARY ICT will help to produce and market more sustainable food with less waste and pollution, due to lower costs of production, as well as product differentiation and segmentation in markets. This is the “green” in green growth; The “growth” in green growth is in the development and introduction of agro-ICT with their specific new markets and changes in the organisation of the food chain; This development reduces current market failures (externalities like pollution) and policy failures (not regulating the internalisation of such social costs) and is another source of increasing welfare (growth); The government can use ICT to better monitor and regulate markets where market failures do not disappear; and Changes in markets or the organisation of the food chain and the specific characteristics of new markets for apps and data will require the attention of policy makers.

The uptake of drones and other data capturing sensors and the platforms to exchange (big) data has the potential to contribute to more sustainable food production, with higher productivity and lower environmental impact. This is the “green” in green growth. In addition, new value chains in agriculture and food will be created based completely on the data, which can have both evolutionary and disruptive elements 11. Besides the market for food (and other agricultural products) there will be developments in the market for ICT services (data, apps, platforms). Those developments and the resulting changes in the organisation of the food chain will provide the “growth” in green growth. The inevitable increased use of ICT and data will have an impact on current markets and value chain arrangements. And this will also create incentives to adjust or develop policies, regulations and rules. In the case of drones (and autonomous tractors as well) it is clear that the new technology requires new regulations. Airspace Authorities around the world are currently updating their rule base to include drones. More importantly are the consequences of new data-driven business models that might cause a new digital divide. Governments have a role to play in developing measures to prevent or at least reduce the digital divide, including creating broadband data-infrastructures and regulations on data privacy and protection. New business models will introduce new entrants in the markets and governments must find their way in dealing with them, as in other domains happens (e.g. Airbnb and Uber). Also, new market governance structures are likely to appear. Examples include direct sales through webshops and online auctions and dynamic pricing (compare with airline tickets). Clearly this is not the first time that the introduction of a new technology creates incentives for governments to update their policies. The question is what is required to mainstream new technologies. This also raises the question of if there is a need to provide extra public support on ICT in agriculture. Are green growth objectives realised more or less automatically in markets now that the technology becomes available? There are big input suppliers (like the equipment manufacturers and agro-chemical industry) and food processors (that need tracing and tracking) who have the 11

“Horizon Scan: ICT & the future of Food and Agriculture”, G. Berti and C. Mulligan, Imperial College London. Commissioned by Ericsson, Sweden. Downloaded from www.ericsson.com

capacity to promote the use of ICT, adapt their products to the needs of farmers and sell their hardware, software and services. Or will their investment not realise the full potential of the green growth objective? Governments have different options for policies to address these impacts, in particular to prevent undesirable outcomes. These options include changing market regulation as well as policies to compensate losers in the change with e.g. social policy or additional education. In market regulation there is a large, classical, array of policy instruments. This can be regulation (competition policy, privacy regulation, property rights, product safety regulation etc.) but also subsidies to promote innovation in cases where the green growth effects are too low. A typical ICTrelated innovation measure which is relevant for the uptake of Big Data is financing and organising broadband connectivity in rural areas. Another government intervention might be to promote the uptake of ICT for green growth with research and innovation support. The following reasons could play a role 12: • Public objectives like food security, employment, and regional development are not automatically guaranteed by the market. More uptake of ICT as a result of innovation and research could deliver such green growth objectives; • In contrast to other sectors, the agriculture and food sector has many SMEs that hardly contribute to investments in knowledge. Investment costs can be (too) high and IPR cannot easily be protected: it is quickly copied in the market. Pooling of funds make sense; • There could be systemic bottlenecks hampering the collaboration between agriculture and the ICT-sector as these sectors are not used to collaborating and lack knowledge on potential win-win situations; • There is a need for common pool investments (infrastructure like ABCDEFs, standards for data exchange etc.) that individual companies will not create; • There could be (negative) external effects of ICT that needs attention: privacy, data ownership, potential discrimination by software algorithms, power balance in the food or software chain, effects on small farms and on remote regions etc.; • There are negative external effects in agriculture that can be solved by ICT more attractively than by regulation (e.g. precision agriculture should benefit the environment, food safety, animal welfare, etc.); • The government is a user of ICT: e.g. the simplification issue in the EU’s Common Agricultural Policy could benefit from better ICT between government and farmers; and • Government supported research could be more efficient with E-science (the science about big data analytics and adjacent tools). Researchers and extension workers will in the future more often use data from farms instead of experimental farms when big data sets of farmers become available; An example is the America Farm Business Network, that between its launch in November 2014 and May 2015 has aggregated data from 7 million acres of farm land across 17 states, and is a benchmarking tool for farmers, able to assess the performance of 500 types of seeds and 16 different crops.

Text taken from EU SCAR AKIS (2015) forthcoming.

6. RECOMMENDATIONS FOR FUTURE POLICY RESEARCH Based on the description of developments and their potential impacts on markets and the organisation of the food chain in the previous chapter, this paper considers ICT as an important technology to realise green growth. However, there are also many open questions related to the policy aspects of the development. The following is a list of issues for policy discussions and future policy research: • Monitoring of the role of ICT on Green Growth in agriculture and food: Analyse experiences (best practices) in application of ICT and its effects on productivity and sustainability; • Environmental policy : ICT makes data capturing and internalising externalities easier. Environmental legislation could be more effective and efficient using such data and could be used as a driver to promote ICT. Some environmental problems might be solved. Are there examples that provide best practices or lessons learned? • Agricultural policy: is ICT optimally used in risk management strategies (e.g. weather information and insurance contracts) and the public advisory service? Is data exchange for agricultural policy between farmers and the government using the same standards and data exchange platforms that farmers use in the food chain (to reduce administrative burden)? Are new types of farming (like urban farming based on LED and ICT technology) to be included in agricultural policy? • Farm Structure policy: Do changes in the organisation of the food chain (more contracts) and in the character of family farms lead to less need for agricultural policy or a focus on a safety net for those who do not adopt the latest technologies? • Regional policy: is the regional infrastructure, e.g. broadband, available and not blocking innovations in the domain of ICT? Are there good examples on how to solve regional bottlenecks? • Competition policy: Are network-effects of platforms to exchange data problematic from a competition point of view? Do ICT-induced changes in the food chain lead to more or less concentration? • Science and Innovation policy: understanding the potential benefits of this technology, does the market lead to an optimal development and adoption of technology or is there a need to promote such innovations? What is the role for the government, given the large interest of input suppliers and food processing companies to introduce and promote this technology? Is there a need to provide certain infrastructure like data standards and data exchange platforms to realise interoperability? Are issues on property rights and privacy issues of data be solvable? Are there examples of open data in agriculture that promote innovations that contribute to green growth? Is research investing enough in big data strategies that can turn the data captured by ICT into knowledge to improve the productivity and sustainability of farming and really lead to green growth?

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