• 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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New technologies: what are they and how do they affect the economy?

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To date, technological change has been key to the economic and social development of the human race. Despite this, the technological revolution that we are currently experiencing, with artificial intelligence (AI) at the helm, is a source of not only wonder but also some misgivings. These misgivings may be due to the new nature of the technologies of the future and the disruptive effects they could have on our economy and society. At the same time, these new technologies could be key to the revival of economic growth that is faltering so much in our European environment.

In this first article of the Dossier, we will go over the different channels through which technology can affect the economic environment.

What is artificial intelligence?

Before discussing the channels through which new technologies affect the economy, it is worthwhile clarifying what we mean by AI, one of the pillars of the technology of the future.

AI has come leaps and bounds since its conception in the 1950s at the University of Dartmouth. The 1990s marked the beginning of a very important stage in the development of AI. However, the real explosion in research into the different techniques used in AI took place in the early 2000s, and a decade later we began to see significant growth in the number of associated patents (see first chart). As an example, 53% of all AI-related patents are subsequent to 2012.1 Some of the most memorable milestones in the development of AI since the 1990s include the moment when, in 1997, the then chess champion Gary Kasparov was defeated by IBM’s supercomputer Deep Blue; when, in 2011, Apple introduced us to its now ubiquitous virtual assistant Siri, and when, in 2012, Google surprised us with the first driverless car.

According to Brookings, «AI consists of machines that respond to stimulation in the same way as humans would, given the human capacity for contemplation, judgement and intention».2 In other words, AI is a software system that reflects human intelligence. More specifically, Brookings talks about its three essential qualities: intentionality, intelligence and the capacity to adapt. Intentionality refers to the faculty of new machines to make decisions in real time using their ability to «feel». This is possible, for instance, thanks to the use of sensors. With regard to intelligence, machine learning (one of the main techniques used in AI), together with data analysis, allows machines to make decisions that we can define as «intelligent». Finally, the capacity to adapt is the ability of new machines to learn as they acquire more information, and to adapt their responses based on what they have learned (they can even learn from the successes and mistakes of other machines, since they are usually interconnected and share experiences between one another).

Effects on the economy

It is well known that technological progress is key to stimulating productivity growth and, therefore, economic growth.3 Even so, technological progress can be a disruptive force in the economy: almost 100 years ago, Keynes coined the term «technological unemployment» to refer to the unemployment caused by workers being replaced by machines. Nevertheless, technological advances also expand our productive capacities, which means that they lead to the creation of new jobs.

In this regard, in a world in which machines not only execute tasks and think, but are also beginning to learn, the possibilities for the automation of jobs can reach unimaginable heights.4

In today’s technological age, experts have focused on four channels through which new technologies can affect the economic environment (see summary table):

1. Technological unemployment. Without a doubt, this is one of the channels that has been most explored in the past and which is also gaining strength today. In general, those who fear the most that machines could replace us as workers do so based on the well-known «substitution effect». In fact, automation has been, is and will be a clear substitute for numerous jobs, which means a destruction of employment in certain sectors and occupations.

The Oxford professors Carl B. Frey and Michael A. Osborne are the authors of one of the works on job destruction that has aroused the most interest, since it suggested that 47% of all jobs in the US were at a high risk of being automated.5 Following their line of thought, at CaixaBank Research we estimated some time ago that, in the case of Spain, this percentage stood at 43%.6

However, three economists from the OECD (Arntz, Gregory and Zhieran) were quick to replicate the study by Frey and Osborne, coming up with a substantially lower percentage taking into consideration that jobs comprise multiple tasks and that only some of them are susceptible to being automated. Under this alternative approach, and with data for 21 OECD countries, the percentage of jobs at risk of being replaced by automation would fall to 9%.7

2. Productivity. Contrasting with the substitution effect is what is known as the complementarity effect. There are jobs in which automation complements the worker. In these cases, machines actually increase workers’ productivity.

In an article that covers numerous classical studies relating technology with productivity, Kevin J. Stiroh, vice president of the Federal Reserve Bank of New York, concluded that information and communication technologies (ICTs) were a significant driver behind the improvements in the US’ productivity at the end of the 1990s.8

More recently, various analyses forecast a significant increase in labour productivity thanks to AI in the medium term. Accenture, for instance, talks about global economic growth rates that could double the current ones by the middle of the next decade, thanks in part to significant increases in labour productivity (of up to 40%) as a result of the use of AI: the new forms of technology complement the labour force, thus increasing its efficiency.9

The link between AI and labour productivity is precisely what we explore in the next two articles of this same Dossier for the case of Spain. Like Stiroh, we conclude that new technologies have been an important factor in the improvements seen in labour productivity in Spain, although not uniformly across all sectors (see the article «The role of new technologies in Spain’s productivity» in this same Dossier for the main results).

3. New products-new jobs. AI also makes it possible to improve the quality of existing goods and services, as well as facilitating the appearance of new products. Again, this effect has a positive impact on employment, in contrast with so-called «technological unemployment».

The production of these new goods and services will be linked to the creation of new jobs. These are jobs that may well belong to the booming technology sectors, as they grow hand in hand with the importance of AI. However, they could also be linked to new needs or business models that may arise thanks to new technologies.

This more positive view of technology, with its beneficial impact for productivity and for new products and services, is defended by economists such as David H. Autor from MIT. In some of his articles, he looks back to point out how the past two centuries of automation and technological progress have not made the worker obsolete.10

4. Supercompanies-competition. Finally, digital technology favours network economies and, therefore, the emergence of supercompanies (the winner-takes-all effect), and this could potentially have clearly negative impacts on the degree of competition. The regulation of such competition in this new environment will need to find a balance between consumer welfare and promoting innovation, ensuring a level playing field, and encouraging greater international coordination in the field of taxation. We discussed all of these elements in the Dossier «Supercompanies: a global phenomenon» in the Monthly Report of March 2019.

In short, it is difficult to predict what path AI will take in the future: the machines of tomorrow can help us by amplifying our capabilities and facilitating the emergence of new goods and services, while at the same time replacing us entirely in some of our tasks. In any case, what is clear is that technology will be a key player in our social and economic environment, with significant disruptive potential. This requires institutions that are well prepared and which encourage technological development, without forgetting that machines must always remain at the service of people.

Clàudia Canals and Oriol Carreras

1. See WIPO (2019). «Technology Trends 2019: Artificial Intelligence». Geneva: World Intellectual Property Organization.

2. See D.M. West (2018). «What is artificial intelligence?». Brookings Report (4 October 2018).

3. Institutions have also been an important ingredient in the pursuit of economic growth. For further details, see D. Acemoglu and J. Robinson (2012). «Why Nations Fail: The Origins of Power, Prosperity, and Poverty, 2». And also A.G. Haldane (2018). «Ideas and Institutions – A Growth Story».

4. See E. Brynjolfsson and A. McAfee (2014). «The second machine age: Work, progress, and prosperity in a time of brilliant technologies». WW Norton & Company.

5. See C.B. Frey and M.A. Osborne (2017). «The future of employment: How susceptible are jobs to computerisation?». Technological Forecasting and Social Change, 114, 254-280. The article appeared in 2013 as a working paper.

6. See A. Morron (2016). «Will the Fourth Industrial Revolution come to Spain?» in the MR02/2016.

7. See M. Arntz, T. Gregory and U. Zierahn (2016). «The risk of automation for jobs in OECD countries». Mimeo OECD.

8. See K.J. Stiroh (2001). «What drives productivity growth?». Economic Policy Review, 7(1).

9. See M. Purdy and P. Daugherty (2016). «Why artificial intelligence is the future of Growth». Accenture.

10. See David H. Autor. «Why are there still so many jobs? The history and future of workplace automation». The Journal of Economic Perspectives 29, nº 3 (2015): 3-30.

 

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