Delivery FAIR Impact in agricultural and Food Security

Biradar, Chandrashekhar

Delivery FAIR Impact in agricultural and Food Security

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2018-03-26

Authors

Biradar, Chandrashekhar

Bonaiuti, Enrico

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Chandrashekhar Biradar, Enrico Bonaiuti. (26/3/2018). Delivery FAIR Impact in agricultural and Food Security. Beirut, Lebanon: International Center for Agricultural Research in the Dry Areas (ICARDA).

Abstract

In recent years research and development organizations have aligned with donors and own frameworks for accountability. Such frameworks have embedded indicators to ensure the measurement of impacts. However, the availability of impact information on different repositories has not been informed by FAIR principles. Learning from available information is becoming more challenging, while knowledge of existing data is in the hands of few individuals in each Organization. Machine learning and artificial intelligence initiatives are trying to address such gaps and limitations. In the last five years, The International Center for Agricultural Research in the Dry Areas (ICARDA) has worked to interoperate internally available resources under the umbrella of the BIGDATA and ICT context, which is defined in its 2017-2026 strategy. As main pillar, the process has involved the analysis of existing metadata elements both as direct and indirect sources. The identified schemas and lists have allowed the team to design internal interoperable protocols in order to share information among different departments and units. The process to ensure findability and accessibility of historical information is hard to achieve when resources are allocated to deliver new products rather than curating and adding value to existing data. Nevertheless, ICARDA is committed to ensure that historical information in its mandated regions and agro-ecological zones is re-used to ensure better modeling, projecting and targeting of interventions related to agriculture and food security. One immediate solution would be to rely on Geo-informatics science and Monitoring, Evaluation and Learning (MEL) system, which may enable the gathering and processing of historical data to inform decision makers on targeted investments. This approach goes beyond basic productivity indicators, while embedding dynamic metrics in the areas of socio-economics and environment. Alignment with the SDG process along with defined indicators will ensure the delivery of FAIR impacts.