Can Big Data transform agricultural productivity in developing countries?
By Andrea Powell, Chief Information Officer, CABI
It seems that every IT conference these days has to include a compulsory session on Big Data, or the Internet of Things (IoT), during which speakers talk excitedly about the prospect of “smart homes” or “smart cities”, where machines will process and exchange vast quantities of data to anticipate your every need. Certainly, the potential for big data to revolutionise food supply chains, or to improve the precision with which farmers spray their crops with fertilizers is unquestionable. However, given the low levels of agricultural productivity in many developing countries, and the lack of legacy infrastructure, surely the greatest prize is to be found in applying such technology at scale in these areas, and sustainably producing enough food to feed a growing global population?
The potential benefits of data-driven decision making in agriculture are well understood, but very little is known about how Big Data is being, can be and should be used to realise these benefits in emerging economies. Indeed, the relatively low level of utilization of advanced technologies is a hotly debated topic in agricultural development circles, and it is clear that developing economies are far from achieving the transformation which Big Data could deliver. Why is this? What is preventing the international development community, national governments and the private sector from overcoming adverse economic, political and cultural obstacles to deploy data-driven solutions to solve some of the most pressing problems in world agriculture?
Of course, there is no simple answer to this question, but a recent book published by CABI goes a long way to explaining the complex dynamics at play, and some strategies for realising the potential for data and analytics to revolutionise agricultural development programmes.
Big Data’s Big Potential in Developing Economies – Impact on Agriculture, Health and Environmental Security by Nir Kshetri of The University of North Carolina, is a thoroughly comprehensive and clearly laid-out exposé of the steps needed to ensure that, as digital technologies and data science advance rapidly in the developed world, their potential to improve livelihoods and raise productivity in developing countries is not overlooked. I have worked for CABI for over 25 years now, and this is the first book we have published which I have actually read. (Perhaps I shouldn’t own up to this, but as a non-scientist with a background in modern languages, our highly science-based research monographs are not usually my choice for bedtime reading.)
Kshetri’s book conveniently defines exactly what we mean by Big Data and how it can be applied to solve problems in agriculture. He also gives a very rational and understandable description of some of the challenges faced in realising the potential of such technology, including the lack of a digital footprint for most individuals in the developing world, the lack of appreciation and skills for accurate data gathering and analysis, the small-scale nature of farming in lesser developed countries and the consequent lack of investment capital. Examples are given of where data has been used to varying effect include the dairy industry in Vietnam, micro-financing initiatives in Africa, the Chinese healthcare system, micro-insurance schemes in Kenya and in the fight against Ebola in West Africa. Each case study includes an assessment of the impact of these initiatives and explores why some were less successful than others.
It is very easy to get carried away by the potential applications for Big Data in a highly sophisticated, digitally-powered country like the UK; we are all now familiar with the “people like you also bought” messages that bombard us when we do our online shopping, or the wonders of digital mapping when trying to avoid a traffic jam. However, this is a timely reminder that there is still a huge amount of work to be done to ensure that such technologies bring benefits to farmers in the developing world, on whom we will be relying to grow enough food to feed 9 billion people in the next few decades.
The potential is undoubtedly huge, but we cannot assume that the same sophisticated approaches which have worked in developed economies will automatically transfer to the developing world, and unless we work with in-country partners to transfer skills in data management, modelling and analysis, the potential of Big Data will remain unrealised. On the other hand, the opportunity for countries to achieve genuine transformation in agricultural productivity through the effective and targeted use of technology is an exciting prospect and a challenge with which we must persevere as a global community.