Research Data and Information

An Olive Grove Near Appia Antica, Rome, Italy

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Infusing new information and knowledge in agricultural systems:

Research Data and Information

My own efforts in standardizing research data sets and trying to use computers to manage them started when I was a veterinary graduate student. In 1984, I standardized and used sheep flock and goat herd health data to plan flock and herd health interventions for the Central Sheep and Wool Research Institute at Avikanagar, India. By 1986, with help from my colleagues at the same Institute, I had standardized the entire data set for animal, especially sheep and goat, production and processing research for the Institute and All India Coordinated Research Projects for Sheep and Goats. Associated with the standardization was the development of an electronic database and development of personal computer tools to analyse and present this data. This included a computer simulation model of a sheep flock to predict financial returns under various production parameters and identify appropriate interventions to optimize productivity.

The online sharing of research data for use in agricultural decision making through the Internet is now emerging. The use of crop modelling and simulation such as CERES Wheat and Maize models and others also contributed significantly to standardization of collection of data related to cultivar, planting density, weather, soil water, nitrogen and other fertilizers on crop growth, development, and yield. Metbroker and similar application have brought use of weather data for agricultural decision making. Similarly application of Geographical Information Systems (GIS) has enabled use of standardized land use data, poverty and food insecurity mapping etc. at the global level. The FAO has a collection of data sets and information in this area. Epiinfo and similar tools have helped standardize human and animal epidemiological information. There is a significant amount of global effort in organizing the documentation and access to plant germplasm data as indicated by Bioversity International. Efforts to standardize and share data related to the Rice Genome has led the way to similar efforts for other crops. As the application of biotechnology and nanotechnology in agriculture spreads, the need to share information at the gene and molecular level of crops and animals globally will also increase. This will require global standards in collecting, collating and accessing data, methods and tools to integrate and analyse them. New challenges in intellectual property rights to the data, information generated and use of information will emerge along with issues of data security and validation.

As I experienced in my efforts to collect research data electronically and share it with colleagues at the Institute and Research System, most efforts in collecting, collating and sharing data sets that contribute to decision making in agriculture include not only the data set but also data standards, data dictionaries, tools and applications to manage, analyse, interpret and present information from the data.

Hans Roslin’s methods of presenting data from public sources illustrate how data and information can contribute to understanding, learning and use of this data in decision making universally. In my opinion, he, in addition to his innovative presentation style, makes a strong case of a movement to standardize and make accessible data collected by public research institutions. Imagine how wonderful it would be to farmers if they are informed using an integrated set of data from crop, weather, pest and other models how their own crop and farm would function with current forecasts of weather, land conditions and cultivar used.

New ICTs, in the form of Internet2 which will improve connectivity and increase the speed of connectivity, data storage and warehousing technologies, computer processing for visualization of data etc. will contribute significantly to increasing global sharing of agricultural data. The critical issue for use of online data for agricultural decision making is in building capacity to enable use of the data and information effectively.

The future of using research data for agricultural decision making lies in creating global standards for agricultural data and its sharing and access, Institutional and National data repositories and data warehouses, appropriate tools and applications to integrate, analyse, interpret and present them. There is a need for National Agricultural Research Systems (NARS), regional and global agricultural research and development organizations to develop these standards and encourage and support the development of data warehouses, tools and applications for agricultural decision making using research data. The main global actors in agricultural research such as the Consultative Group on International Agricultural Research and its International Agricultural Research Centres (IARCs) and FAO should take a lead in this area. The Global Forum on Agricultural Research has a critical role in awareness building, advocacy and investment in this area by all concerned.

The standards for agricultural data, in my opinion, should be developed by the scientific professional societies who will also create a validation system, maybe through peer evaluation, for the data. These societies will also have an important role to play in protecting intellectual property of data sources and managers.