Potential of Big Data for agriculture and rural development
In order to overcome barriers related to BD adoption, policymakers should ensure various enabling conditions for the creation, availability, and use of data
Dr. Vasanthi Rajendran
Big Data (BD) has attracted extensive interest due to its potential tremendous social and scientific value. It is playing an increasing role in agriculture and rural development. More and more key development institutions, research organisations and individuals are recognising the budding potential of big data to facilitate innovation, and create impact on smallholder agriculture. This potential touches on, for example, precision farming with very efficient water and fertiliser use, food security coordination through tracking, tracing and transparency and personalised health and nutrition advice. Big data gathered using mobile phones, drones, internet systems and satellites is being used in projects to analyse existing behaviours or topics such as demography, climate change, market-based interactions, crops, livestock and forest infrastructure, saving time and improving productivity.
Data has become a valuable global commodity. But it is much more than simply information: in expert hands, it is intelligence. Already, analysts are finding ways to turn big data the immense stocks of information collected in computers worldwide into an invaluable resource for planning and decision-making. It is helping accelerate the development of robust responses to some of the most pressing challenges of our time: climate change/variability, food insecurity and malnutrition, and environmental degradation. It is transforming the world of genomics and crop breeding and revolutionizing disciplines from climate modelling to agronomy. It is helping refine policies and improve lives.
The smart and effective use of data will be one of the most important tools for achieving the United Nations Sustainable Development Goals. Big data represents an unprecedented opportunity to find new ways of reducing hunger and poverty, by applying data-driven solutions to on-going research for development impact.
Perhaps the greatest advantage offered by BD in the context of development is that it helps us gain a better understanding of the extent and nature of poverty and devise appropriate policy measures. For instance, mobile data can make it possible to better understand the dynamics of rural residents. The call detail record (CDR) and other information can provide insights into the rural population, which would help forecast the needs for toilets, clean drinking water, and infrastructures .Alternative data collection and analysis techniques such as surveys have a very low degree of usefulness for such purposes, which may take months and even years for getting results and are often out of date.
Developing world-based farmers face difficulties in meeting the quality and safety standards set by the developed world. In this regard, the availability of easily accessible data that include digital records of farming activities such as the amounts of seeds and pesticides will obviously play a major role in documenting quality standards of agricultural products. It would be interesting to assess the above examples related to the use of data in farming activities in developing countries in terms of BD dimensions. Obviously, higher volume of data on farming activities is available than in the past. For instance, data such as farmers credit history and the amounts of seeds and pesticides used was not available in the pre BD-environment. As to the data speed, near-real-time data and information on farmers needs and capabilities are available. This means that financial institutions, produce buyers, and other relevant actors can fulfil farmers needs more quickly than in the past. Regarding the variety, most data currently used in farming-related activities is structured data. Such data can be combined with unstructured data. For instance, farmers can upload pictures and videos related to a problem they are facing, which can be analyzed by experts to offer customized advice.
While large growers can afford specialized machineries, small farmers are not in a position to do .The conditions that stimulated the growth of BD in farming industry such as the widespread adoption of mechanized tractors; genetically modified seeds, computers, and tablets for farming activities are less prevalent in developing countries. Most smallholders in developing countries are not in a position to do so. Smallholder farmers often have no means to access the data and cannot interpret it. A main concern is that BD collection efforts will only benefit big and well-educated farmers.
Accurate and actionable data require considerable technical skills to handle data mining and analysis method and system. The lack of human resources and expertise represents another major barrier to the implementation of BD projects. Data scientists are both in short supply and expensive to employ in developing economies .Most of the top BD companies are from the industrialized world and developing competitive indigenous companies in the BD area is not an easy task for developing countries.
There is a lack of appropriate database systems for agribusiness development, agriculture management, and produce distribution. A BD attempt is greatly hampered by the lack of reliable infrastructure to collect information. Developing countries have limited capacity to develop, generate, disseminate, and effectively use climate data and information. National institutions, leadership, and the civil society are inherently weak and cannot determine the types of climate data and information needed for agriculture and other economic activities. Among the problems faced by policymakers and practitioners to work more effectively to respond to climate changes and other climate related effects concern extremely low number of meteorological stations for climate data collection and the lack of digitization of the data.
The lack of information has been a main barrier to an effective implementation of healthcare systems. For instance, while there is a rising prevalence of diabetes in many developing countries , there is no data available to measure the effects beyond intermediate outcomes such as the number of people trained; percentage of health centres providing education; or development of training material and guidelines.
It is especially necessary to introduce policies, procedures, and interventions to ensure the privacy and confidentiality of sensitive data. Data consumption and exchange are no less important than data production and analysis. The utilization of BD in key development areas hinges critically upon the availability of manpower with BD competency. It is thus important for national governments and international agencies to direct more efforts towards developing BD manpower. Guidelines, interventions, supports, and incentives are needed to encourage sharing existing data. In this regard, much of the valuable data that is relevant for the development context is often with the private sector..The data being used in a number of developmental purposes can be considered as BD. Preliminary evidence indicates that BD is likely to help better utilize the scarce resources and can help deal with the various sources of inefficiency that have been frequently cited by critics as among the key obstacles for development in developing countries. Uses of BD that lead to positive social and economic outcomes and those that benefit socially and economically disadvantaged groups need to be promoted. Responsible uses of BD also require protecting peoples dignity and legitimate expectations of privacy and economic interests.
BD obviously offers a number of potential benefits and vast possibilities in developed economies. Nevertheless, developing economies are at a nascent stage and far from a full utilization of the great potential of BD. Benefiting from BD requires a drastically different approach. In order to overcome barriers related to BD adoption, policymakers should ensure various enabling conditions for the creation, availability, and use of data. The lack of BD-related skills and competency underscores the importance of moving the focus beyond the numbers of technological devices to the strengthening of national technological capacity to use BD.
It would be critical for the developing worlds governments to get support from key stakeholders such as researchers, international agencies, software makers and data intensive sectors to create and utilize relevant, development-related data and information. Collaboration and cooperation among these stakeholders are essential to foster a data ecosystem for development. In the meantime, policymakers, academics, and other stakeholders should make the most of what is available.
The writer is Director (Information and Communication) Centre on Integrated Rural Development for Asia and the Pacific (CIRDAP)
