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

    CatalanSpanish

    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.

    Plantilla

    plantilla_article_vs05

    Temática
    Etiquetas
    Miniatura
    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%.

    p 26
    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

    p 29

    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
    Desactivado
    Destacado Analisis Sectorial
    Desactivado
    Destacado Área Geográfica
    Desactivado

The role of new technologies in Spain’s productivity

Content available in

Imagine a group of friends discussing current affairs in a bar. One of them exclaims: «The other day I read that computers are now able to identify pictures making fewer mistakes than a human being!» This would likely be followed by other examples about the world of possibilities offered by new technologies. It is also very likely that many of us have had a conversation like this one, which shows the extent to which we are surprised by technological advances, as well as the magnitude of their economic and social impact.

Beyond this anecdote, the question before us is whether this impact is of the magnitude it appears to be and, consequently, whether new technologies have the potential to give a boost to the future growth of the Spanish economy. In this article we will see that new technologies have indeed favoured the growth of Spain’s labour productivity in the past and that they could do so again in this new technological era we are entering.

An initial analysis of the relationship between the degree of penetration of new technologies and labour productivity shows that there is a positive correlation between the growth of the two variables in the past 20 years.1 In addition, this correlation appears to be more pronounced among economic activities in the services sector (see first chart).2

A closer look at the key determining factors of productivity

Unfortunately, the chart we have seen above offers an incomplete analysis of the issue. The reason for this is that there may be other factors which are making a positive contribution to growth in productivity but, at the same time, have a positive correlation with the degree of penetration of new technologies. By way of example, imagine a world in which a sector’s productivity depends solely and exclusively on the education and training of its workers, and that in sectors with more qualified workers there is a higher incidence of new technologies. In this example, the correlation between advances in new technologies and labour productivity would be positive, but this would be as a result of the level of education and training of each sector’s workers, not the result of new technologies.

To take this into account, we conducted a more complete statistical exercise in which, in addition to considering variables such as labour productivity and new technologies for each sector, we included in the analysis other variables that might influence the results, such as all other categories of physical capital.3 The key variable of our analysis is the elasticity of growth in labour productivity relative to the growth of capital in new technologies. Put simply, this is the sensitivity of productivity growth to a 1 pp increase in the growth of capital in new technologies.4

Finally, our analysis distinguishes between aggregate elasticity and elasticity disaggregated by sector: on the one hand, we have estimated the elasticity for all sectors of the economy and, on the other, we have also estimated the elasticities based on certain characteristics of each sector. Specifically, we have estimated disaggregated elasticities for four groups: low tech industries, high tech industries, low tech services and high tech services.5

The results of the empirical analysis (see second chart) show how, in the aggregate case, we obtain an elasticity of around 0.12, which is by no means negligible. As an example, suffice to say that the estimated elasticity of the «rest of capital» factor – i.e. all categories of capital stock not classified as new technologies, which includes elements that are as important for a country’s productivity as industrial plants and all kinds of machinery – is 0.26. Nevertheless, this aggregate result hides significant disparities between sectors, with elasticity varying between 0 and 0.25. As one might expect, the highest elasticities are associated with the two groups of sectors that we classify as high tech.6

The role of capital in new technologies
in economic growth

Having obtained an estimate of the impact new technologies have on labour productivity, we conducted an exercise that shows more clearly the importance of this form of capital for the economy. Specifically, we break down the growth of labour productivity into three factors: the contribution from capital in new technologies, that of the remaining capital – the sum of both constitutes the total stock of physical capital in the economy – and that of the residual component, which we refer to as «other». This latter category includes elements ranging from human capital to openness to trade, temporality, and other factors that fall under what is referred to as total factor productivity (TFP).7 We show the results in the third chart.8

For the economy as a whole, we can see that the growth of new technologies explains slightly more than the full 14% of cumulative growth in labour productivity between the periods 1996-1998 and 2014-2016. The remaining capital explains around 10 pps, which are offset by the negative contribution from the «other» component. This result is surprising for two reasons: the first is the high contribution from technological capital, and the second is the negative contribution from the «other» component.

With regard to the first element, it should be noted that the average annual growth of this component over the aforementioned period was much higher than that of the «rest of capital» component: 6.1% versus 1.3%, respectively. As such, although the elasticity of the capital in new technologies is lower than that of the rest of capital, its high growth explains its significant contribution to productivity growth. On the other hand, the negative contribution to the growth of labour productivity during this period from the «other» component is consistent with other estimates that show a negative contribution from the TFP.

Looking at the contributions to growth by sector in greater detail, we see some very different results. If we compare the rates of labour productivity growth, the two sectors that stand out the most are those classified as high tech, both for the industrial and the services sector. However, the sources of growth have been very different between one and the other. While in high tech services the main source of growth has been the growth of capital in new technologies, in the case of high tech industry, capital in new technologies has had a more modest contribution. In contrast, the services sector classified as low tech hardly experienced any growth at all in its labour productivity during the period in question. That said, this was due to a negative contribution from the residual «other» component, which was offset by the contributions from the growth of both types of capital. Finally, the growth in the productivity of the low tech industrial sector is mainly explained by growth in the «rest of capital» category.

Can we expect new technologies to provide a new boost to growth?

Before delving into conclusions, we want to provide the reader with a theoretical exercise aimed at answering the question we raised at the beginning of the article: to what extent can new technologies act as a spur for European economic growth, and in particular for Spain? In previous Dossiers we have explained that the global economy is facing a period of lower productivity growth than in other historical expansionary periods.10 This section offers some scenarios that allow us to consider the extent to which the introduction of these new technologies can spur the growth of labour productivity in Spain.

We consider two scenarios. The first, more pessimistic one assumes that the growth of capital investment in new technologies will be half that historically observed in the period 1996-2016, while the second, more optimistic scenario assumes a growth that is 50% higher than the historical average (see table).

A 50% increase (optimistic scenario) in the growth of investment in new technologies relative to the historical average would entail a boost to productivity growth (and, therefore, to GDP) of slightly more than 0.3 pps per year. While this growth differential may seem small year on year, accumulated over a 10-year period it means that GDP would be 3.5% higher compared to a scenario where investment in new technologies evolves in line with the historical average. In terms of GDP per capita, this would be equivalent to a difference of around 1,250 euros.

This optimistic scenario we have just presented may even prove to be conservative, if we consider that the potential of new technologies may be going through a transition phase in which businesses and consumers are still learning how to use them efficiently. This means that, in the future, the productivity growth associated with investments in new technologies could be greater than in the past, as applications are consolidated, new business models mature, workers’ training improves and productive factors are reallocated. Therefore, our exercise may even be underestimating the impact of new technologies on future economic growth by taking as a benchmark a period of time that could entail a technological «transition».

In conclusion, taking into account the results presented in this article, should we continue to make advances in the use and diffusion of new technologies in order to boost economic growth? In principle, the answer is «yes», but let us remember that the first article of this Dossier pointed out that, besides its positive impact on productivity, new technologies can have disruptive effects for the labour market (in the form of job destruction) and for the productive structure (by favouring the emergence of supercompanies). A response with all the necessary nuances thus requires an analysis of these other dimensions, to which we will devote a future Dossier.

Now, if we focus on what we have learned in this empirical analysis, it is undeniable that the introduction of new technologies has had a significant impact on labour productivity in Spain over the last two decades. This impact is not homogeneous across sectors, but rather it is greater in those which produce goods and services that are considered high tech. Even so, at the aggregate level for the economy as a whole, the effect has been considerable: in the absence of investment in these technologies, labour productivity in Spain would have been practically stagnant during the period between 1996 and 2016.

Clàudia Canals and Oriol Carreras

Clàudia Canals and Oriol Carreras

1. We define labour productivity as the gross value added per hour worked. We define the degree of penetration of new technologies as the stock of capital per hour worked in software and databases, research and development, computers and telecommunications equipment (according to EU KLEMS). As discussed in the second article, this is a broad approximation for new technologies.

2. We classify economic sectors according to the classification used by Eurostat: low tech industry and services, and high tech industry and services. This subdivision excludes the agricultural sector (CNAE code 2009: A). See the second article of this same Dossier for further details on how Eurostat performs this classification.

3. More specifically, we estimate the following panel regression model:

\(\Delta\ln\;Lprod_{i,t}=\alpha\;+\;\gamma_{i\;\;}+\;\delta_t\;+\;\beta\Delta\;\ln\;K_{i,t}^{AI}\;+\;\theta'\;\Delta\ln\;x_{i,t}\;+\;\nu_{i,t}\)

where the indices i and t refer, respectively, to the economic sector and year. Also, the variable Lprod refers to labour productivity, KAI to the capital stock in new technologies, x to the other control variables (in particular, we include all the capital stock that is not classified as KAI, the percentage of workers with at least a university degree or equivalent level of education, the degree of openness to trade and the percentage of workers with a temporary contract), γi refers to the unobserved fixed effect in each sector, and δt refers to the unobserved fixed time effect. The variable of interest is β. These results are robust to the extent of the inclusion of a temporal trend variable at the sectoral level.

4. By way of example, if the elasticity is 0.5, this means that if the growth of capital in new technologies were to increase by 1 pp, the growth of labour productivity would rise by 0.5 pps.

5. See note 2.

6. A lower elasticity does not mean that the sector in question is lagging behind in terms of technological innovation. The usefulness of new technologies depends on their ability to provide value, and it may well be that these new technologies are not yet providing significant value in certain sectors, but that they may do so in the future.

7. Total factor productivity encompasses all the productivity growth that cannot be explained by the accumulation of productive factors.

8. To calculate the contributions, we multiply the elasticities of capital by its growth.

9. See, for example, C. Fu and E. Moral-Benito (2018). «The evolution of Spanish total factor productivity since the global financial crisis», Occasional papers nº 1808, Bank of Spain.

10. See the Dossier «Technological change and productivity» in the MR02/2018.

    im02-20_d3_01_en.png
    im02-20_d3_02_en.png
    im02-20_d3_03_en.png
    im02-20_d3_04_en.png