The buzzword in today’s world is big data. Digital communication tools such as; cell phones and social media generate massive amounts of data which are used in both developing and developed countries at an increasing rate. It is increasingly being perceived to proffer solutions to development problems especially in developing countries where there is a tremendous proliferation of technological devices particularly mobile phones. Development experts divulge that, with the use of these devices, huge volumes of information are obtained which can be used for policy measures.
This data according to United Nation Global Pulse, (2012) is likely to be a useful indicator of human well-being although it was not traditionally used in development issues. High volume, high velocity, high variety, high variability and complexity are the characteristics that make Big Data useful in the global World and in development (Gartner, 2018 and sas.com, 2018).
For instance, Bays (2014), explained how geo-coded cell phone transaction data was used in an emergency social system project in Nairobi, Kenya to predict the growth of slums.
The information collected was used by the government for appropriate resource allocation towards infrastructural development. Also, in Africa, there is an increasing collaboration between NGOs, the private sector and farmers association through training, education and access to agricultural information to enhance and improve farming activities. This enables small-scale farmers to gain access to information which can go along the way to improve their activity thus leading to greater productivity. Countless development literature tells how successful access to Big Data has been especially within the health and agricultural sectors.
However, this essay will analyse literature to ascertain the opportunities and challenges that are associated with using technology (Big Data) in the agricultural and farming sector.
In the agricultural and farming sector, development experts believe that Big Data can lead to improving productivity which is beneficiary to the Sub-Saharan African Countries where over 60% of its labour force is in the agricultural sector (Oluoch-Kosura, 2010). UNCTAD’s World Investment Report of 2009, revealed that 65 countries were at risk of alarming food shortages and famine, with about 900 million people globally being malnourished (UNCTAD, 2009). In addition, Oluoch-Kosura, (2010) opines that farmer’s productivity can be improved by up to 40% if effective farming operations such as early plating/weeding, good land preparation and harvesting techniques and good housing and feeding for livestock can be implemented. Big Data is believed to have the potential of improving these conditions and it is augured that, in the farming sector, it is the source of the next revolution (Bunge, 2014). According to the World’s largest seed company, Monsanto, annual Worldwide crop production can be increased by about US$20 billion through tailoring information and advice to farmers (Bunge, 2014).
In the aspect of precision agriculture, which is vastly deployed in industrialised countries, technological equipment such as drones, weather forecast instruments (GPS), robotics, autonomous vehicles, GPS-based soil sampling, telematics, variable rate technology and software have been used to collect data related to the condition of soil, crop yields, seeding rates and other farming variable information (Bunge, 2014; agfundernews.com, 2018). It is no news that farming is a labor intensive activity, and using these technologies, famers have improved on their productive, efficiency and cost management. However, there are still task which can not be performed by the existing algorithm thus human experts are needed to perform tasks requiring decision making, especially when it concerns to extreme weather conditions and unknown soil types, (Bunge, 2014; agfundernews.com, 2018). A combination of both human expert and algorithms data is transformed in to customised information and sent to the famers usually advising on information related to fertilizers, herbicides, pesticides and other relevant farming measures.
While the industrialised nations big famers have access to these Big Data facilities, a comparative analysis with their counterparts in less industrialised countries reveal a huge gap in the Big Data ecosystem. Most importantly, the industrialised nations have a long history of data production and consumption in the agricultural sector whereas this is just evolving in the less industrialised nations. In the early 2000s, DuPont had been making use of farm-level data (Bunge, 2014). Likewise, the use of iPad and tablets to monitor the progress of agricultural activities among farmers have been a long-term tradition. A rich ecosystem of Big Data for tracking; weather and market analysis, satellite imaging and weather datamining in industrialised countries has been enabled by diverse firms which are not visibly present in less industrialised countries.
Another example which illustrates the wide use of Big Data for farming in industrialised countries is the use of global positioning system satellite to guide tractors and combines. Doering (2014) explained how a drone was used by a farmer in Iowa to monitor the yields on his 900 arce of farm to see if the crops where affected by topographical changes or any other factors. This example and many more illustrate the extent of diffusion and adoption of Big Data in the agricultural sector in industrialised countries with Bunge (2014), affirming that, a significant increase in productivity has been reported by famers who implemented data driven prescriptive planting. Whereas big farmers can purchase specialised machineries, Doering (2014), this remains a dream for smaller farmers. Similarly, while some farmers such as those in the industrialise countries have access to mechanised tractors, genetically modified seeds, computers and tablets as a result of favourable growth conditions in the farming industry, those in the developing countries lag due to the absence of these growth conditions. Some farmers are not able to have access to farming information talk more of knowing how to interpret and apply the information. Thus, only educated farmers can fully benefit from using Big Data.
The availability of near-real time data and information regrading farmer’s needs and capabilities is another opportunity offered by Big Data to value chain partners in developing countries to facilitate farming activities. The AgriLife cloud-based platform is one good example in this domain. This application is available via mobile phones and it supports the collection and analysis of famer’s production capability and history. Through this application, distant and rural farmers can have access to fast, easy and efficient information related to their farming activities. It also used for financial services and as a link between farmers, produce buyers, mobile operators and other related agents (Yeoman, 2013). Yeoman supports his view by explaining how the Uganda’s Farmers Centre (FACE) adopted AgriLife for its farming services. Through this platform, information was collected from 10,000 famers who were users of the service to build a transaction and credit history database with which the famers could be assessed for suitability for credits, seeds, fertilizers, pest control chemicals and other value-added services. With AgriLife, farmers can get faster and just in-time information than was the case in the pre AgriLife era. They can also get customized advice from experts through uploading pictures and videos of their farming challenges.
Without considerable technical skills to handle data mining and analysis methods and systems, there is limitation to accurate and actionable data. This thus leads to a major barrier to the implementation of Big data projects, as a result of lack of human resources and expertise. According to Kroes, (2013), the European Union economies reported a huge skill shortage for data-related manpower. Noticeably, there is a short supply of data scientist and more even expensive to employ one in both industrialised and less industrialised nations. Presently, most Big Data companies are in the industrialised nations while the less industrialised nations are finding it difficult codeveloped competitive indigenous Big Data companies. Neelie Kroes, the EU competition commissioner, confirmed that 17 out of 20 Big Data companies where US based while two were European (Kroes, 2013). Concurrently, Korolov, pointed out that the US is a base for 15 most powerful Big Data companies while only one was based in Europe (Korolov, 2013).
The collection of farming data does not go without fear as the farmers raise issues related to information miss-use by the data collection companies. For example, members of the American Farm Bureau Federation (AFBF) were cautioned about their information being used by seed companies since these companies had interest in higher crop yields with Big Data (Bunge, 2014). Likewise, farmers could be influenced with their data collected by big companies such as Monsanto to buy branded equipment, seeds and sprays thus increasing their profit margins (Seppala, 2014). Synonymous to the level of attention accorded to immigration and water reforms, is the gathering of data from sensors on combines, tractors and other faming equipment by large seed companies. Another key issue association with data collection is information exposure, as farmers have expressed concern over their information being used by their competitors. Which can eventually lead to unhealth competition and disputes.
As an upshot of the above discussion, are security and privacy concerns associated with Big Data. With security and privacy regulations implemented in most industrialised countries, it is still evident that famers still express concerns over their privacy. Most of the Big Data companies have data protection policies and regulations to put a check on the data that is harvested from farmers. For instance, DuPont, Monsanto and other Big Data corporations confirm that data collected by them is not for sale to any third party but used strictly for the purpose of providing services to the farmers (foxnews.com, 2014). While according to some companies, they claim to obtain express permission from customers before any data sharing can take place. However, in less industrialised countries the farmers are not able to have any control over their information as there is hardly any data protection policy or regulation measures implemented by thus the possible misuse of farmers and citizen information (Bunge, 2014). This therefore means that, nascent institutionalization settings surround Big Data related issues and big businesses in less industrialised countries have leverage and exploitation opportunities over farmers.