• 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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Will the Fourth Industrial Revolution come to Spain?

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If you think that a robot is unlikely to replace you in your job, perhaps this article will prove you wrong. Your opinion might be based on the fact that, in the past, automation of the economy was limited to repetitive tasks such as mental calculation (calculators), copying texts and images (computers and printers) and cleaning (dishwashers).1 However, forthcoming technological improvements will permit the automation of non-repetitive tasks that, to date, are the exclusive domain of humans. This article analyses the impact of such changes on the labour market, considered by some experts to be the Fourth Industrial Revolution.

A robot's behaviour is governed by an algorithm; i.e. by a list of procedures established in advance by a human programmer. For example, you can instruct your computer to sell 100 shares only if they go above 50 euros. Traditionally, for a robot to carry out a task a human programmer had to understand the sequence of steps necessary to carry it out and be able to specify them clearly. However, recent improvements in the sensory and processing capacity of machines, together with the development of big data and artificial intelligence, are allowing automation to spread to almost all kinds of non-repetitive tasks such as driving vehicles (the Google driverless car) and medical diagnosis (IBM's Watson robot). Thanks to big data robots can make use of a large database to test and learn which algorithms work best. Moreover, they can share their experiences and thereby learn from the errors and advances made by others. For example, at the Memorial Sloan-Kettering Cancer Center of New York, IBM's Watson robot provides diagnoses and treatments from an extensive database of medical reports and scientific articles. Consider also Google's car, which shares the information from its sensors with a highly detailed road map, specifying the exact position of streets, signs and obstacles, to decide in real time whether to turn, brake or accelerate according to what other cars and pedestrians are doing.

Technology has advanced so much that a study by McKinsey estimates that, today, 45% of the tasks existing in the US could be automated.2 But we must not confuse tasks with jobs: a job or a profession is made up of many different tasks such as social interaction or physical exercise. To evaluate the impact on employment we must analyse how many tasks from each profession are at risk of being automated, which is precisely what two professors from Oxford University have done, namely Carl B. Frey and Michael A. Osborne.3 Their analysis identifies three groups of tasks which technology will stillnot be able to carry out in the next two decades: perception and manipulation in unstructured environments,4 creative intelligence (making a joke) and social intelligence (persuading someone). According to the relative importance of thesethree types of task, Frey and Osborne calculated the probabilityof each profession being computerised.5 For the US they produced a list with 702 professions and the probability of computerisation associated with each of them. If we convert their US classification to the Spanish case we can estimate the effect on a list of 485 professions in Spain. The first table shows examples of professions according to the risk of automation.

As we have already mentioned, it is estimated that technology is already capable of automating skilled professions (see the risk faced by accountants, financial analysts and economists), while those in which human interaction and creativity are more important (family physicians, musicians) are the most protected. This is also illustrated by the first graph, where we have classified professions into nine large groups. Scientists (creativity) and managers (social interaction) face little risk while office workers are concentrated in the high risk group.

On the whole, according to our estimates 43% of the jobs that currently exist in Spain have a high risk (with a probability higher than 66%) of being automated in the medium term while the rest of the jobs are shared equally among the medium risk (between 33% and 66%) and the low risk group (below 33%).

However, we must not confuse the potential to automate the economy with the disappearance of jobs. Technology destroys professions but not the opportunity to work. Automation of the professions we know today offers the chance to redirect the nature of work, releasing workers so they can dedicate themselves to new activities in which they can develop all their potential, as exemplified by the vacuum cleaner and washing machine relieving people from housework. Most workers spend a large part of their time doing tasks in which they do not take advantage of their comparative advantage over robots,6 so there is great potential to create new professions if institutions and individuals take advantage of this opportunity.7 Robots have a great capacity for logic and handling big data but inspiration, intuition and creativity are far beyond their scope.8

Technological improvements provide the chance to enrich society as a whole but, apart from technological potential, there are important economic factors determining their adoption and impact on society. On the one hand companies will adopt technology only if it is cheap enough. For example, in the last few decades the reduction in the cost of computers has led to workers with intermediate skills being replaced when they carried out repetitive tasks that are easy to specify in an algorithm, contributing to the polarisation of the labour market and increased inequality.9 This leads us to another relevant economic aspect: the distribution of new wealth. Our data indicate a negative correlation between the likelihood of a profession being automated and its annual median wage, suggesting a possible increase in inequality in the short term.

In the long term, in a world where robots were capable of carrying out absolutely all tasks, the distribution of income and wealth –rather than resource scarcity– would be the main raison d'être for economists.10 If such a profession exists in the future.

Adrià Morron Salmeron

Macroeconomics Unit, Strategic Planning and Research Department, CaixaBank

1. See also the article «Automation: the dread of workers», in this Dossier.

2. McKinsey & Company (2015), «Four fundamentals of workplace automation», McKinsey Quarterly, November 2015. Breaking down each task into multiple capabilities (e.g. for the task «receiving clients» one needs capabilities such as the perception and transmission of emotions), they evaluate the percentage of capabilities involved in each task that current technology is able to reproduce.

3. Frey, C. and Osborne, M. (2013), «The Future of Employment: How Susceptible Are Jobs to Computerisation?», Working Paper.

4. For example, it is much more difficult to program a robot to find a book in a back room (unstructured environment) than on the organised shelves of an Amazon warehouse.

5. A group of robotics researchers, brought together by Oxford University, analysed 70 professions and assigned to each case a probability of 1 if they thought that all the tasks of the profession in question could be carried out with the most advanced technology we have today, and 0 in any other case. They then extrapolated this classification to a universe of 702 occupations with a probability-based model of allocation based on nine variables describing the degree of perception, manipulation, creativity and social intelligence required to carry out each task within an occupation.

6. McKinsey's report estimates that, at present, only 4% of jobs in the US are demanding in terms of creativity.

7. See also the article «How to take advantage of the positive impact of technological change on employment?» and «The unavoidable metamorphosis of labour market: how can education help?» in this Dossier.

8. See Autor, D. H. (2015), «Why Are There Still So Many Jobs?», Journal of Economic Perspectives, page 3-30.

9. See also the article «How to take advantage of the positive impact of technological change on employment?» in this Dossier.

10. See also Keynes, J. M. (1930), «The Economic Possibilities for our Grandchildren». In such a world moral principles would be redefined and deciding what to do with our lives, free from material restrictions, would be the biggest challenge we would face.

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