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


    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.



    Área geográfica

    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

    p 28

    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.

    Destacado Economia y Mercados
    Destacado Analisis Sectorial
    Destacado Área Geográfica

The consumption of each generation in normal times... and in times of pandemic

Has the COVID-19 affected the consumption habits of each generation or the same types of goods in equal measure? How did we consume before and during the pandemic?

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Portada del IM05 2021

Each generation has its own consumption pattern. Young people, more prone to change and eager for novelty, tend to be the focus of attention for advertisers and marketing firms with the goal of attracting them and anticipating future consumer trends. However, it is the adult population that accounts for the majority of consumption: in 2019, the population from 30 to 64 years of age accounted for 73% of total card expenditure.1 Also, those over the age of 65 have greater purchasing power than in the past and represent an increasing percentage of the population: in 2020, 1 in every 5 Spaniards were aged 65 or older, whereas in 2035, 26.5% of the population will be in this age range.2

We know that the COVID-19 crisis has had a big impact on consumption, both because of the mobility restrictions themselves and because of the increased economic uncertainty.3 According to data from the national accounts maintained by the National Statistics Institute (NSI), private consumption in Spain fell by 12.4% in real terms in 2020, an even more pronounced decline than that of GDP (10.8%). But has the COVID-19 crisis affected the consumption habits of each generation or the same types of goods in equal measure? How did we consume before and during the pandemic?

To answer these questions, given that public sources such as the NSI’s household budget survey are only available up until 2019, we turned to CaixaBank’s internal data, which already allow us to analyse how the consumption of each generation evolved in 2020 during the pandemic.4 To do this, we analysed the card payment data of 13.4 million CaixaBank customers, completely anonymised and classified into three different age groups: young (from 18 to 29 years of age), adult (30 to 64) and senior (65 and over). Their consumption habits are representative of the consumption carried out by each generation in Spain.5 We shall begin by studying them in normal times, in order to then compare them with what happened following the fateful events of 2020.

  • 1. According to internal consumption data for cards issued by CaixaBank (excludes cash withdrawals at ATMs).
  • 2. National Statistics Institute. «Population projections. 2020-2070».
  • 3. See, for example, J.M. González, A. Urtasun and M. Pérez (2020). «Consumption in Spain during the state of alert: an analysis based on payment card spending». Economic Bulletin of the Bank of Spain, nº 3, 2020. Or N. Cox et al. (2020). «Initial Impacts of the Pandemic on Consumer Behavior: Evidence from Linked Income, Spending, and Savings Data». BFI Working Paper.
  • 4. For a general approach, see the Focus «Analysing private consumption during the COVID-19 crisis» in the MR07/2020.
  • 5. The proportion of average consumption and that of each generation observed in the data from CaixaBank’s Consumption Tracker is very similar to that of the NSI’s household budget survey for types of goods that are comparable between the two databases (food and beverages, clothing and footwear, or restaurants and hotels).
Consumption of millennials, adults and senior citizens in normal times

In Spain, in periods free of restrictions on activity (such as 2019, the year we take as a benchmark), young people, adults and older people have different tendencies in their consumption of each type of good, as a result of their differing preferences and resources (see first chart). Most of the consumption carried out by seniors is devoted to essential goods (food and pharmacy goods, 44%), whilst adults and young people spend a lower percentage on this category (36% and 22%, respectively). In contrast, young people are the generation that spends the most on leisure and catering (32%), far more than adults and seniors. The proportion of total consumption devoted to durable goods (electronics, textiles, furniture, etc.) is more similar between generations, accounting for around a quarter of the total, albeit with differences depending on the type. For instance, young people devote roughly half of their consumption of durable goods to textiles and sports (47%), compared to 41% among adults and 35% in the case of seniors. Finally, the proportion of consumption allocated to tourism and transportation is the closest between the generations, both representing around or slightly below 10%, although the spending levels differ.6

  • 6. A senior’s average card spending in 2019 was half that of an adult’s (49%), while that of a young person was 91% of an adult’s.
Spain: distribution of consumption by generation and type of expenditure
Consumption of millennials, adults and senior citizens in times of pandemic

During the first few months of the pandemic, the restrictions on activity made a significant dent in the consumption of all generations (measured in year-on-year terms). The biggest declines were registered in April, amounting to between 60% and 80% in leisure and catering and in durable goods, and exceeding 85% in the case of transportation and tourism (see second chart). The only exception was the improvement in the consumption of essential goods, which registered increases of more than 50%. This increase was delayed by a couple of months in the case of seniors, possibly because relatives and friends initially did the shopping for their elders in many cases.

Spain: averag e expenditure per client of each generation and by type of expenditure

The recovery in the following months, however, was more varied by age group. The consumption of essential goods among older people increased more than among young people and adults, and more persistently too. In the last quarter of 2020, the consumption of essential goods among seniors was 37% higher than in Q4 2019, 10 pps more than the increases registered among young people and adults. On the other hand, the consumption of the elderly in the categories of leisure and catering and in tourism recovered more slowly than in the other generations, and only partially. In the last quarter of 2020, their average consumption in leisure and catering was 23% lower than in the previous year, while in the case of tourism it was 66% lower. In contrast, young people and adults allocated a significant portion of their spending in the summer to leisure and catering and to tourism, categories on which they had spent very little in the preceding months. It therefore appears that there was greater intratemporal substitution of consumption in catering and tourism for essential goods among seniors than in the other generations. In contrast, young people and adults followed a pattern of intertemporal substitution, postponing spending on leisure and catering and on tourism from Q2 to Q3 rather than replacing it with other categories of goods. These differences in behaviour show that the recovery of pent-up demand for leisure and tourism was more concentrated in these latter two age groups in 2020. However, the senior group could play a greater role in the summer of 2021, when they will have been vaccinated and will have greater freedom of movement. On the other hand, as the autumn of 2020 progressed and the new restrictions imposed to control the second wave were tightened, all generations once again reduced their consumption in all types of goods.

In short, the differences in the recovery of consumption show that preferences and priorities matter, and that post-pandemic consumption could be different for each generation. In the following articles we analyse these patterns in greater detail for the cases of e-commerce, the use of cash and digital payment methods, and we compare the evolution of each generation’s consumption with that of its income.

    Long-term trends


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    We analyze the causes and consequences of inequality and what policies can foster inclusive economic growth that is distributed equitably in society.

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    The keys to understanding how new technologies are substantially transforming the economy and how society works.