• Digitalisation of the agrifood sector: what does Twitter tell us?

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    Technology is advancing at a frenetic pace and offers the agrifood chain a large number of opportunities to make its production more efficient and sustainable. Moreover, the arrival of COVID-19 has shown that the most digitalised companies were able to continue their activities more readily than the rest. In this article we examine the degree of popularity of the different digital technologies used in the primary sector and agrifood industry based on a text analysis of over 2 million tweets on Twitter. All these technologies are essential to create a connected ecosystem that will make up the Food Chain 4.0 of the future.

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    The unexpected arrival of the pandemic has shown that the most digitalised companies were more prepared to adapt to the new situation and were able to continue to operate much more smoothly than the rest. There is no doubt that, in this new environment, the digital transformation of companies is now unavoidable in order to boost their competitiveness.

    Big data, robotics, the internet of things and blockchain are just some examples of the new digital technologies gradually being adapted by firms, particularly in the agrifood sector. Technology is advancing at a frenetic pace and is offering the agrifood chain a large number of opportunities to produce more efficiently and sustainably. However, statistical information on the degree to which such technologies have been taken up, and the most comprehensive official statistical source1, does not provide information on the primary sector. Below we present a novel analysis of the «popularity»  of new digital technologies in the agrifood sector based on data from Twitter.

    • 1. Survey on the use of information and communication technologies (ICT) and e-commerce in companies, compiled by the National Statistics Institute.
    Twitter as a source of information to detect future trends

    Data from Twitter can be extremely valuable in detecting new trends as it allows us to analyse the popularity of certain terms according to how frequently they appear in tweets. However, it is true that «talking about something» is not the same as successfully implementing the various digital technologies in a company's recurring operations. For this reason the results presented below should be interpreted simply as an indication of new trends that may be taking root in agrifood companies.

    Data from Twitter allow us to analyse how popular the different digital technologies

    are in the agrifood sector according to how often they are mentioned in tweets.

    For this study, data was processed from over 24 million tweets sent by individual users and digital media during the period 2017-2019. Among these, 2 million corresponded to the agrifood sector. Using natural language processing techniques, the tweets were categorised according to mentions of different digital technologies and to the business sector.2 The key to obtaining relevant data from social media is to first define «seed» words or phrases to identify texts corresponding to each of the business sectors, as well as «seed» words or phrases related to the different digital technologies of interest.3 Using a machine-learning algorithm, other words and phrases related to the concept in question that were not initially included were also identified, thus broadening the spectrum of texts analysed. At this stage, it is important to carefully screen for polysemous words (i.e. those that have more than one meaning, such as the word «reserva» in Spanish, which can be used to refer to a hotel booking as well as an aged wine).

    • 2. This analysis was carried out in collaboration with Citibeats, a company specialising in unstructured natural language processing.
    • 3. For example, the «seed» woods and phrases used to identify big data were: analytics, arquitectura de sistemas (system architecture), data mining, database, inteligencia empresarial (business intelligence), Python and SQL, among others (as well as the term big data per se).
    What is the degree of digitalisation of the agrifood sector according to Twitter?

    To assess the agrifood sector's degree of digitalisation according to data from Twitter, we first need to know how common tweets about digitalisation are in other business sectors. The most digitalised industry according to our analysis is the information and communication technologies (ICT) sector: 3.2% of the sector's tweets contain terms related to digitalisation, a result that is not surprising given the very nature of the industry. Next comes finance and insurance with 2.7% of the tweets.

    This percentage is obviously lower in the primary sector at 0.6% but it is similar to the 0.7% for professional, scientific and technical activities. In the case of the agrifood industry, the percentage of tweets on digitalisation is only 0.3%, very close to the basic manufacturing sector (which includes the textile, wood, paper and graphic arts industries), with the lowest percentage among the sectors analysed, 0.2%.

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    Which digital technologies are most popular in the agrifood sector according to Twitter?

    The wealth of data obtained from Twitter allow us to identify the most popular digital tools in each business sector according to how frequently they are mentioned in the tweets examined. According to our analysis, a large proportion of the primary sector's tweets about digitalisation tend to include issues related to big data (45% of all tweets about digitalisation). One clear example of the application of big data in the sector can be found in «precision agriculture» techniques which require large amounts of data to be analysed to optimise decisions and thereby increase production and, in turn, ensure sustainability. These techniques are used, for instance, to calculate the irrigation requirements of crops by taking into account climatic conditions (sunlight, wind, temperature and relative humidity) and crop characteristics (species, state of development, planting density, etc.). To carry out this calculation, real-time updated meteorological data, a large computing capacity and fast data transmission speeds are all required for an automatic irrigation system to be properly adjusted. This technology helps to use water more efficiently, a highly relevant aspect in areas with a Mediterranean climate that are extremely vulnerable to climate change and where water is in short supply.

    Big data, the internet of things and robotics are the most popular technologies in the primary sector,

    indispensable for advancing the application of precision agriculture techniques and smart automated farming.

    Other popular technologies in the primary sector are the internet of things (16% of tweets) and robotics, including drones (10% of tweets). The new digital technologies promise to revolutionise the field of agriculture and stockbreeding by the middle of this century, the same as the mechanisation of farming in the xxi century. Agricultural Machinery 4.0 (which is closer to the robots in science fiction films than to the tractors we are used to seeing on all farms in the country) helps to increase productivity whilst also improving working conditions in the field. This trend towards more automated agricultural tasks has become stronger in the wake of the coronavirus pandemic, as the difficulty in recruiting seasonal workers due to international mobility restrictions has led to increased interest in robotics and agricultural automation. In fact, companies that manufacture robots for agriculture have seen a sharp increase in orders, such as robots that pick strawberries while removing mould with ultraviolet light.14 

    The use of drones warrants particular attention as this has grown exponentially in recent years and applications are increasingly widespread: from the early detection of pests and the aerial inspection of large areas of crops to locating wild boar with heat-sensitive cameras to prevent the spread of African swine fever to domestic pigs.5

    • 4. See Financial Times Agritech «Farm robots given Covid-19 boost», 30 August 2020.
    • 5. See http://www.catedragrobank.udl.cat/es/actualidad/drones-contra-jabalies

    The popularity of various digital technologies in the agrifood sector

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    Blockchain is the technology that stands out most in the food sector (30% of the total number of tweets on the sector's digitalisation) and this comes as no surprise as it has many different applications for the food and beverage industry. Producing a chain of unalterable, reliable records, blockchain makes it possible to guarantee the complete traceability of products throughout all the links in the food chain. Simply scanning a QR code provides access to all the data regarding the origin, production method, veterinary treatments received, ingredients used, etc. A large number of agrifood companies are already experimenting with blockchain as it offers clear benefits in terms of transparency regarding origin, product quality and food safety, aspects that are increasingly valued by consumers. Blockchain technology is also being used to limit food waste, another essential challenge for the sector.

    Blockchain enables the digital verification of food products,

    making them traceable throughout the links in the food chain.

    Compared with other sectors, which tools are particularly significant for the agrifood industry?

    There are some digital technologies that are not very popular across all economic sectors, perhaps because they have a more limited or specific range of application. These are technologies that, despite having a low percentage of tweets in absolute terms according to our study, may be relatively popular for a particular sector compared with the rest.

    To detect such cases, we have calculated a new metric, namely a concentration index which takes into account the relative popularity of technologies in a sector compared with the rest of the sectors.6 By using this methodology, we have found that the primary sector continues to stand out in terms of big data. Specifically, the primary sector concentrates 9.2% of the total number of tweets mentioning big data made by all sectors, a much larger proportion than the 3.1% share of primary sector tweets out of the total number of tweets analysed (as can be seen in the following table, in this case the concentration index is 3). We have also determined that the sector is particularly interested in the internet of things, as already mentioned, but have discovered that nanotechnology is also a relatively popular technology in the primary sector. In other words, although only 3.8% of the tweets in the primary sector deal with nanotechnology, this percentage is high compared with the 1.7% share of nanotechnology tweets out of the total (in other words, this technology is not very popular in general across all sectors but is slightly more popular in the primary sector than the others). This find is not surprising since genetic engineering is one of the fields in which technology has advanced most in order to boost crop yields. For example, by optimising the yield of vines it is possible to develop plants that are much more resistant to extreme weather conditions and pests.

    • 6. The concentration index is calculated as the ratio between (1) the percentage of tweets related to a particular technology and sector out of the total tweets for this technology, and (2) the percentage of tweets by a sector out of the total tweets of all sectors. Values above 1 indicate the technology is relatively more popular in that sector.

    Concentration index for tweets related to each technology in comparison with the other sectors

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    Finally, virtual and augmented reality is also a relatively popular technology in
    the agrifood industry.
    Specifically, the agrifood industry concentrates 6.2% of the total virtual and augmented reality tweets made by all sectors, a percentage that more than doubles the 2.5% share of primary sector tweets out of the total number of tweets analysed (the concentration index is equal to 2.5 in this case). This technology uses virtual environments (virtual reality) or incorporates virtual elements into reality (augmented reality) that provide additional knowledge and data that can be used to optimise processes. At first it may be surprising that this technology is relatively popular in the agrifood industry but its uses are spreading as the industry implements digital technologies in its production processes, in the so-called Industry 4.0. One specific example of how this technology is used is in repairing breakdowns. When a fault occurs, operators can use augmented reality goggles to follow the steps contained in virtual instruction manuals that are projected onto the lens to help resolve the incident. The glasses recognise the different parts of the machine and visually indicate to operators where they should act to solve the specific problem.

    There are numerous examples of new digital technologies being applied in the agrifood sector. We are witnessing a revolution that is destined to transform the different links in the food chain: from the exploitation of data and the use of drones to make harvesting more efficient to implementing blockchain technology to improve the traceability of the final products that reach our homes. In short, the future will bring us the Food Chain 4.0, a totally connected ecosystem from the field to the table.

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The sharing economy and the labour market

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Most of our parents and grandparents spent their whole lives working at the same company where they began their career –something which is unthinkable for young people joining the labour market today. Non-standard labour relations1 are becoming increasingly common and go some way towards explaining the current high rate of part-time, temporary and self-employed workers (19.4%, 12.2% and 13.7%, respectively, in 2017). Furthermore, the rise of the sharing economy has led to the creation of a new type of non-standard employment: «on-demand» employment. Digital platforms facilitate this type of employment by reducing transaction costs and allowing supply and demand for employment to be matched in real-time. To draw an analogy with human relations, a standard job would be like a marriage, whereas working in the sharing economy would be akin to going on a series of dates arranged through a dating app.

Specifically, so-called on-demand employment can be grouped into two broad categories: gig employment and cloud working. Gig employment is characterised by individuals using digital platforms to search for customers to whom they can offer their services, some of which are more labour-intensive (such as a delivery service) while others are more capital-intensive (such as home rentals). Cloud working, meanwhile, involves companies making requests online for tasks or services they need. Online job markets, such as Amazon Mechanical Turk, Freelancer and Upwork, are used to request a wide range of services, such as computer programming, design, translation, administrative or accounting tasks, etc., many of which were not so easy to outsource up to now. These services often consist of simple micro tasks that can be carried out quickly, such as filling in a survey, while others involve more complex jobs, such as designing an entire website.

Despite the visibility of some jobs that have been generated through the sharing economy (just think of the meal delivery bicycles that have appeared in most cities), it is difficult to estimate precisely how many people work in the sector. Official statistics, such as the labour force survey (LFS), do not currently include details about these jobs,2 nor do the digital platforms provide any detailed data on how many workers use them. However, there are studies that try to estimate the number, using big data algorithms. Harris and Krueger (2015) estimated that around 0.5% of the US workforce was employed in the sharing economy. According to De Groen and Maselli (2016), the European equivalent would be around 0.05% (about 100,000 active workers). Other estimates based on surveys, tax data or banking transactions point towards higher percentages, albeit with measures that are not directly comparable. Around 22% of the US population has offered services through online applications at some time,3 while in the EU the figure lies somewhere between 1% and 5%.4 Employment in the sharing economy therefore represents only a small percentage of the labour force, but it is growing fast.5

Employment in the sharing economy offers clear advantages in terms of flexibility. Individuals decide when and where they work, which allows people who otherwise would not have worked, due to mobility issues or other restrictions, to do so. In addition, the low cost of entry helps individuals who previously would have been easily excluded from the labour market to gain access. For example, according to Über, 25% of its drivers in Paris were unemployed before starting work, and many resided in deprived banlieues, where unemployment is high. Yet, by using applications of the sharing economy, they obtained employment and income in times of need.

However, there are disadvantages too: the working conditions of on-demand jobs tend to be worse than in other kinds of employment. In particular, the median wage for these jobs is well below the minimum wage, and the number of weekly hours worked is half that of all other workers (see second chart). In fact, for many of them, such is the supplementary and intermittent nature of the work that they often do not even consider that they are working. As for working different jobs, or moonlighting, performing a multitude of tasks makes it difficult to specialise and develop a career. Furthermore, the greater job insecurity associated with this type of work can have health-related consequences, in terms of anxiety, stress, etc.

One topic which generates controversy is the employment status of individuals who perform on-demand work. At present, the work performed in the sharing economy is legally considered self-employed work, since it shares many characteristic elements. The individual chooses when they work and how many hours they do so for, they provide the resources needed to render the service, such as a vehicle they own, and they charge only for each service provided. However, some elements of their work have more in common with that of an employee, particularly with regard to pricing and service conditions, which are often dictated by the applications. In addition, these companies operate using online reputation tools, such as ratings, and they design algorithms that affect the likelihood of an individual being chosen to provide a service, even having the power exclude them from the platform altogether. In doing this, the platforms are exercising control mechanisms on service providers that are more typical of those used on employees and, like employees, the service providers are obliged to follow them.

With regards to labour rights, those working in the sharing economy do not tend to have the right to collective bargaining or the right of association to negotiate the conditions of service delivery with the platforms, nor are they covered by other fundamental workers’ rights such as non-discrimination. Furthermore, the idiosyncrasy of the work performed - involving a multitude of services, clients, etc. - limits the social security coverage they can enjoy and the accumulation of their rights.

All of these issues have led to a debate about whether a specific legal status for these workers is necessary. Several proponents, such as Harris and Krueger,6 have argued for a status of «self-employed worker» that would include certain rights, such as the right to organise, but not others, such as minimum wages or unemployment insurance. In fact, some platforms are now beginning to offer an occupational accident insurance to their «workers» or the right to organise. Some countries, on the other hand, already have a framework in place for «dependent self-employed workers». Although such frameworks were put in place prior to the arrival of the sharing economy, they could still serve as a good fit.7

While the debate remains open, and will no doubt depend on each sector and particular platform, it is important to note that many characteristics of working in the sharing economy are similar to other non-standard work, hence they can be considered together.8 For example, people working non-standard jobs have difficulties accumulating welfare benefit rights, and this has led to increased support for the proposal to link rights to individuals, rather than to jobs.9 The rights could therefore be accumulated and moved from one job to another, such that they would not be lost, which would also facilitate flexibility in the labour market. In short, the need to adapt social welfare is not just limited to employment in the sharing economy, but also extends to all the «non-standard» jobs that are becoming increasingly commonplace in 21st-century economies, in which globalisation and technological developments are changing the way we produce... and work.

Josep Mestres Domènech

CaixaBank Research

1. According to the International Labour Organization, the following categories are considered non-standard work: part-time work, working under a temporary contract and being self-employed.

2. For the time being, Canada is the only country that included questions about the collaborative economy in its LFS, and the data obtained indicated that 0.5% of the labour force in 2016 worked in it (Statcan, 2017).

3. See Burson-Marsteller, Aspen Institute and TIME (2015), «The collaborative economy survey».

4. European Parliament (2017), «The Social Protection of Workers in the Platform Economy».

5. See Farrell and Creig (2016), «The Online Platform Economy: What is the growth trajectory?», JP Morgan Chase Insights.

6. See D. Harris and A.B. Krueger (2015), «A Proposal for Modernizing Labor Laws for the Twenty-First-Century Work: The “Independent Worker”», The Hamilton Project, DP 2015-10.

7. In Spain, self-employed workers are considered to be economically dependent if over 75% of their sales are made to a single client.

8. See V. De Estefano (2016), «The rise of the “just-in-time workforce”: On-demand work, crowdwork and labour protection in the “gig-economy”», ILO.

9. OECD (2018), «Opportunities for All: OECD Framework for Policy Action on Inclusive Growth».

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