Big Data – Big Agriculture

There are few questions more central to the future of humankind than the future of food security, farming and agriculture. With regards to this, the hopes are high for technology, big data and “smart farming”. Many believe that big data will be “the fuel that drives the next industrial revolution, radically reshaping economic structures, employment patterns and reaching into every aspect of economic and social life” (Spratt, 2015, p.6).

In a Ted-talk from 2015, Jim Ethington, technologist and entrepreneur with long experience from building data analytics and machine learning products, focuses on how big data can be used to help solve world hunger problems. With an ever increasing population on this planet – assumed to reach over 9 billion in 2050, in combination with a shift towards a more protein heavy diet, we will need to increase our food production with more than 50% in the upcoming years. In order to do this we need to increase the productivity of the land that we already farm. Ethington looks into Africa, a continent where the gap between the potential production and what is actually being produced is greater than anywhere else in the world. Whereas other developing countries have managed to increase their production, the production in African countries has remained stagnant. Why has Africa remained “stuck in time”? Ethington speaks about lack of access to information as a main reason. Even though resources and subsidies have been invested for a long time in African agriculture, the average farmer still uses inefficient and non-sustainable methods and often sticks to old routines. So what has gone wrong? Ethington sums it up as: “our inability to put crucial information in the hands of farmers. To really empower each farmer to the make the right decision about their fields for their farm and put them in the driver seat to drive those changes”.

Big data and technology can create tools that can be used to help farmers make good decisions that allow them to adopt new practices. As the world is becoming digitized, we know more about the interaction between crops, weather and soil than we have ever done before. But, says Ethington: “all this computational power is worthless unless you can turn it into a simple actionable solution that you can put in the hand of a farmer”.  As one interviewee in a recent case-study put it: “[big data proponents] need to realise that not everyone in the world is middle class, lives in New York and owns an iPhone.” (Spratt, 2015, p. 29). The crucial question is, what value does big data provide small-scale farmers in developing countries? After all, technological solutions are not one-size-fits-all and the AgTech movement will not be successful until its benefits extend to all the world’s farmers. Therefore: “It’s time for technology visionaries to look beyond the Farm Belt and invest in supporting the other 90 percent of the world’s farmers” (Foote, 2016).

Ethington brings up the importance of using accessible technology that people already have and know how to use, such as mobile phones. The use of mobile technology is already wide-spread in many African countries. An example is in Nigeria where the government created an “eWallet” system so that it could offer input subsidies directly to farmers. During the first year this program enabled 1.7 million farmers to produce an additional 8.1 million metric tons of food. “These innovations weren’t born in Silicon Valley but in places closer to the farm gate, like Kenya’s Silicon Savannah” (Foote, 2016). The UN Global Pulse Project Series is a collection of case studies of 20 data innovation projects covering global issues ranging from public health to climate change, food security to employment. The majority of the case studies listed in this project actually focus on mobile technology for harvesting information of food prices and other issues related to food security and agriculture.

For whom?

Even though technology can serve as a vehicle for providing data, analysis and communication, there are many other aspects to consider when it comes to agriculture in developing countries. For instance, many farmers still lack access to some of the most basic agronomic practices, inputs and technologies and infrastructure: trucks, roads, and storage facilities (Foote, 2016). In the paper Big Data and International Development: Impacts, Scenarios and Policy Options, Stephen Spratt & Justin Baker look at potential impacts of big data in relation to developing countries. The authors address the importance of a nuanced approach: “Those who argue for the benefits of big data often adopt an evangelical tone, while opponents tend to stress the dystopian nature of a ‘big data future’. These visions of heaven or hell can sound preordained, but there is nothing inevitable about the impacts that big data will have, or how these will affect different groups” (Spratt, 2015, p.7). With regards to agriculture, the authors mention how companies specializing in data collection, analysis, prediction and distribution are increasingly turning data into a commodity. Two of the greatest agricultural companies in the US, Monsanto and John Deere, collect and distribute big amounts of data from farmers with the goal of increasing productivity.  This big movement of data can also have negative impacts, particularly with regards to privacy. Consumers and farmers are not necessarily able to control what corporations do with their data once they have been collected, an issue which is even more relevant in the context of developing countries where legal and institutional frameworks to ensure data privacy are more often lacking (Spratt, 2015, p.14).

Policy and framework

Spratt & Baker claim that in the absence of a strong framework and policy, some of the more “dystopian visions” regarding big data and surveillance may come true, and “the power of large, data-hungry corporations to manipulate personal data (…) for own commercial ends can only continue to grow” (Spratt, 2015, p.35). In order to minimize the risks of negative effects of big data use in developing countries, the authors suggest a number of policy interventions that can be implemented. Some of these are:

  • Ensure that citizens have access to internet-enabled devices, the internet and affordable power.  Enable communication and interactions via online platforms such as Skype, Facebook, Twitter and Instagram. Connectivity benefits will only materialize if hardware is available and affordable, and internet connections are of high enough quality).
  • Address the English language bias of big data early warning programmes, and increase their ability to tap into grass-roots real-time data sources. Big data needs to be able to monitor grassroots-level activity, particularly on social media, before they are filtered through news outlets. A problem is that a lot of these data are not in English, and big data remains heavily skewed towards English language material, despite improvements in translation software and analytics.
  • Generate revenues to invest in ‘smart’ urban design and transport infrastructure. The fact that these are currently lacking in many instances, creates the potential for intelligent design from the outset, rather than restructuring existing systems, which is inherently more difficult. For this potential to be realised, however, the resources needed to invest in these systems still need to be found.
  • Do not allow the ability to improve the ‘intelligence’ by which social systems, infrastructure and government services can be delivered detract from problems of inequality. Designing policy to ‘optimise the status quo’ (…)  risks entrenching inequalities and also distracting attention from the need to address them.
  • Develop successful domestic tech businesses to prevent ‘brain drain’. This is related to the issue of the ‘digital divide’ – to how efforts to narrow the skills gap  and build capacity could result in (expensively) trained workers emigrating to a developed economies.

(Spratt, 2015, p. 29-30).

The use of technology and big data is a double-edged sword, and even though there are risks and pitfalls, at the same time many of the largest potential benefits of big data are to be found in developing countries. In his talk, Jim Ethington aim at a holistic solution, where not only increased productivity is the main goal, but also a move towards empowerment and social change through greater access of information. These goals are of course closely connected and intertwined. Ethington ends his talk by quoting the American biologist, humanitarian and Nobel laureate, also sometimes called “agriculture’s greatest spokesperson” Norman Borlaug: “The first essential component of social justice is adequate food for all mankind”.

 

REFERENCES

Ethington, J. (2015, July 28). How can Digital Agriculture Feed Nine Billion People [Video file]. Retrieved from https://www.youtube.com/watch?v=owl8keTUioo

Foote, W. (2016). Can Silicon Valley’s big bet on agriculture help small-scale farmers in developing countries? Retrieved from: http://www.forbes.com/sites/willyfoote/2016/03/16/can-silicon-valleys-big-bet-on-agriculture-help-small-scale-farmers-in-developing-countries/#d3b3ea07874a

Spratt, S., Baker, J. (2015).  Big Data and International Development: Impacts, Scenarios and Policy Options. Brighton: IDS. Retrieved from: https://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/7198/ER163_BigDataandInternationalDevelopment.pdf?sequence=1

UN (2016) UN Global Pulse Series. Retrieved from: http://www.unglobalpulse.org/blog/big-data-development-action-global-pulse-project-series

 

 

This entry was posted in Big data, Policy and tagged . Bookmark the permalink.